Editorial survey: swarm intelligence for data mining
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paper_title: Swarm-Bots and Swarmanoid: Two Experiments in Embodied Swarm Intelligence

paper_content:

Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, it focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. The characterizing property of a swarm intelligence system is its ability to act in a coordinated way without the presence of a coordinator or of an external controller. Swarm robotics could be defined as the application of swarm intelligence principles to the control of groups of robots. In this talk I will discuss results of Swarm-bots, an experiment in swarm robotics. A swarm-bot is an artifact composed of a swarm of assembled s-bots. The s-bots are mobile robots capable of connecting to, and attached to each other and, when needed, become a single robotic system that can move and change its shape. S-bots have relatively simple sensors and motors and limited computational capabilities. A swarm-bot can solve problems that cannot be solved by s-bots alone. In the talk, I will shortly describe the s-bots hardware and the methodology we followed to develop algorithms for their control. Then I will focus on disconnecting from, other s-bots. In the swarm-bot form, the s-bots are the capabilities of the swarm-bot robotic system by showing video recordings of some of the many experiments we performed to study coordinated movement, path formation, self-assembly, collective transport, shape formation, and other collective behaviors. I will conclude presenting initial results of the Swarmanoid experiment, an extension of swarm-bot to 3- dimensional environments.
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paper_title: Beam-ACO - hybridizing ant colony optimization with beam search: an application to open shop scheduling

paper_content:

Ant colony optimization (ACO) is a metaheuristic approach to tackle hard combinatorial optimization problems. The basic component of ACO is a probabilistic solution construction mechanism. Due to its constructive nature, ACO can be regarded as a tree search method. Based on this observation, we hybridize the solution construction mechanism of ACO with beam search, which is a well-known tree search method. We call this approach Beam-ACO. The usefulness of Beam-ACO is demonstrated by its application to open shop scheduling (OSS). We experimentally show that Beam-ACO is a state-of-the-art method for OSS by comparing the obtained results to the best available methods on a wide range of benchmark instances.
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paper_title: AntNet: Distributed Stigmergetic Control for Communications Networks

paper_content:

This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.
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paper_title: The Ant System: Optimization by a colony of cooperating agents

paper_content:

An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
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paper_title: Particle swarm optimization

paper_content:

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
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paper_title: Flocks, herds and schools: A distributed behavioral model

paper_content:

The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the natural world. But this type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually. The simulated flock is an elaboration of a particle systems, with the simulated birds being the particles. The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course. Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it by the "animator." The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds.
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paper_title: Ant colony system for a dynamic vehicle routing problem

paper_content:

An aboundant literature on vehicle routing problems is available. However, most of the work deals with static problems, where all data are known in advance, i.e. before the optimization has started.
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paper_title: Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem

paper_content:

This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to TSPs. Ants cooperate using an indirect form of communication mediated by a pheromone they deposit on the edges of the TSP graph while building solutions. We study the ACS by running experiments to understand its operation. The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and we conclude comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.
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paper_title: An ant system algorithm for the mixed vehicle routing problem with backhauls

paper_content:

Ant system algorithms have been used successfully to solve many hard combinatorial problems. In this paper we introduce an ant system method to solve the mixed vehicle routing problem with backhauls. Some enhancements to the general characteristics of ant system algorithms are proposed. We concentrate on the way the candidate list is constructed, a look ahead scheme to incorporate into the visibility function and efficient rules to deal with local as well as global updating. Computational results on test problems are reported and compared to known results.
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paper_title: Beam-ACO - hybridizing ant colony optimization with beam search: an application to open shop scheduling

paper_content:

Ant colony optimization (ACO) is a metaheuristic approach to tackle hard combinatorial optimization problems. The basic component of ACO is a probabilistic solution construction mechanism. Due to its constructive nature, ACO can be regarded as a tree search method. Based on this observation, we hybridize the solution construction mechanism of ACO with beam search, which is a well-known tree search method. We call this approach Beam-ACO. The usefulness of Beam-ACO is demonstrated by its application to open shop scheduling (OSS). We experimentally show that Beam-ACO is a state-of-the-art method for OSS by comparing the obtained results to the best available methods on a wide range of benchmark instances.
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paper_title: A Population Based Approach for ACO

paper_content:

A population based ACO (Ant Colony Optimization) algorithm is proposed where (nearly) all pheromone information corresponds to solutions that are members of the actual population. Advantages of the population based approach are that it seems promising for solving dynamic optimization problems, its finite state space and the chances it offers for designing new metaheuristics. We compare the behavior of the new approach to the standard ACO approach for several instances of the TSP and the QAP problem. The results show that the new approach is competitive.
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paper_title: Ant Colony Optimization: Overview and Recent Advances

paper_content:

Ant Colony Optimization (ACO) is a metaheuristic that is inspired by the pheromone trail laying and following behavior of some ant species. Artificial ants in ACO are stochastic solution construction procedures that build candidate solutions for the problem instance under concern by exploiting (artificial) pheromone information that is adapted based on the ants’ search experience and possibly available heuristic information. Since the proposal of the Ant System, the first ACO algorithm, many significant research results have been obtained. These contributions focused on the development of high-performing algorithmic variants, the development of a generic algorithmic framework for ACO algorithms, successful applications of ACO algorithms to a wide range of computationally hard problems, and the theoretical understanding of properties of ACO algorithms. This chapter reviews these developments and gives an overview of recent research trends in ACO.
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paper_title: AntNet: Distributed Stigmergetic Control for Communications Networks

paper_content:

This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.
---
paper_title: The Ant System: Optimization by a colony of cooperating agents

paper_content:

An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.
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paper_title: Ant system for Job-shop Scheduling

paper_content:

The study of natural processes has inspired several heuristic optimization algorithms which have proved to be very effective in combinatorial optimization. In this paper we show how a new heuristic called ant system, in which the search task is distributed over many simple, loosely interacting agents, can be successfully applied to find good solutions of job-shop scheduling problems.
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paper_title: Exact And Approximate Nondeterministic Tree-Search Procedures For The Quadratic Assignment Problem

paper_content:

This article introduces two new techniques for solving the Quadratic Assignment Problem. The first is a heuristic technique, defined in accordance with the Ant System metaphor, and includes as a distinctive feature the use of a new lower bound at each constructive step. The second is a branch-and-bound exact approach, containing some elements introduced in the Ant algorithm. Computational results prove the effectiveness of both approaches.
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paper_title: Traffic Lights Control with Adaptive Group Formation Based on Swarm Intelligence

paper_content:

Traffic problems nowadays are increasing because of the growing number of vehicles and the limited resources provided by current infrastructures. On intersecting roads, huge number of vehicles leading to congestion and heavy traffic. Traditional traffic controls (warning signs, stop signs, etc.) are used in most areas but these are sometimes inadequate to address the problem. Since the expansion of the traffic network is no longer a socially attainable solution, the existing control systems have to be used in a more intelligent way in order to increase the traffic throughput and decrease total travel times. In our project we are controlling traffic using ‘SWARM intelligence’. Using SWARM intelligence makes the system decentralized i.e. no need of centralized control room. We are also displaying the traffic at next junction in the direction of travelling so that people aware about the traffic of next junction. The methods that are used in this project are design the circuit, write a coding, simulation, synthesis and implement in hardware. In this project, AVR studio Software was chosen to write assembly C coding and MATLAB is used to write coding for DIP part.
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paper_title: Applying the Ant System to the Vehicle Routing Problem

paper_content:

In this paper we use a recently proposed metaheuristic, the Ant System, to solve the Vehicle Routing Problem in its basic form, i.e., with capacity and distance restrictions, one central depot and identical vehicles. A “hybrid” Ant System algorithm is first presented and then improved using problem-specific information (savings, capacity utilization). Experiments on various aspects of the algorithm and computational results for fourteen benchmark problems are reported and compared to those of other metaheuristic approaches such as Tabu Search, Simulated Annealing and Neural Networks.
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paper_title: Data Mining with an Ant Colony Optimization Algorithm

paper_content:

The paper proposes an algorithm for data mining called Ant-Miner (ant-colony-based data miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts as well as principles. We compare the performance of Ant-Miner with CN2, a well-known data mining algorithm for classification, in six public domain data sets. The results provide evidence that: 1) Ant-Miner is competitive with CN2 with respect to predictive accuracy, and 2) the rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2.
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paper_title: Decompositional Rule Extraction from Support Vector Machines by Active Learning

paper_content:

Support vector machines (SVMs) are currently state-of-the-art for the classification task and, generally speaking, exhibit good predictive performance due to their ability to model nonlinearities. However, their strength is also their main weakness, as the generated nonlinear models are typically regarded as incomprehensible black-box models. In this paper, we propose a new active learning-based approach (ALBA) to extract comprehensible rules from opaque SVM models. Through rule extraction, some insight is provided into the logics of the SVM model. ALBA extracts rules from the trained SVM model by explicitly making use of key concepts of the SVM: the support vectors, and the observation that these are typically close to the decision boundary. Active learning implies the focus on apparent problem areas, which for rule induction techniques are the regions close to the SVM decision boundary where most of the noise is found. By generating extra data close to these support vectors that are provided with a class label by the trained SVM model, rule induction techniques are better able to discover suitable discrimination rules. This performance increase, both in terms of predictive accuracy as comprehensibility, is confirmed in our experiments where we apply ALBA on several publicly available data sets.
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paper_title: Density-Based Heuristic for Rule Discovery with Ant-Miner

paper_content:

Ant-Miner is an Ant colony Optimization (ACO) algorithm for rule discovery in database. The method was originally introduced by (Parepinelli, Lopes, & Freitas, 2002) and has shown comparable results to existing methods in the literature. The heuristic value used in Ant-Miner is based on the concept of entropy. While this concept is motivated by information theoretic measures, it seems that with the idea of using pheromone values in ACO, the ants can recover easily without this computationally expensive concept. In this paper, we present a heuristic value based on a simple sample density estimation that is easy to compute and does not resuire the calculations of logs as in Ant-Miner. We show that our proposed method, in this initial analysis, performs exactly the same as Ant-Miner.
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paper_title: Classification With Ant Colony Optimization

paper_content:

Ant colony optimization (ACO) can be applied to the data mining field to extract rule-based classifiers. The aim of this paper is twofold. On the one hand, we provide an overview of previous ant-based approaches to the classification task and compare them with state-of-the-art classification techniques, such as C4.5, RIPPER, and support vector machines in a benchmark study. On the other hand, a new ant-based classification technique is proposed, named AntMiner+. The key differences between the proposed AntMiner+ and previous AntMiner versions are the usage of the better performing MAX-MIN ant system, a clearly defined and augmented environment for the ants to walk through, with the inclusion of the class variable to handle multiclass problems, and the ability to include interval rules in the rule list. Furthermore, the commonly encountered problem in ACO of setting system parameters is dealt with in an automated, dynamic manner. Our benchmarking experiments show an AntMiner+ accuracy that is superior to that obtained by the other AntMiner versions, and competitive or better than the results achieved by the compared classification techniques.
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paper_title: Separate-and-Conquer Rule Learning

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This paper is a survey of inductive rule learning algorithms that use a separate-and-conquer strategy. This strategy can be traced back to the AQ learning system and still enjoys popularity as can be seen from its frequent use in inductive logic programming systems. We will put this wide variety of algorithms into a single framework and analyze them along three different dimensions, namely their search, language and overfitting avoidance biases.
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paper_title: MAX-MIN Ant System

paper_content:

Abstract Ant System, the first Ant Colony Optimization algorithm, showed to be a viable method for attacking hard combinatorial optimization problems. Yet, its performance, when compared to more fine-tuned algorithms, was rather poor for large instances of traditional benchmark problems like the Traveling Salesman Problem. To show that Ant Colony Optimization algorithms could be good alternatives to existing algorithms for hard combinatorial optimization problems, recent research in this area has mainly focused on the development of algorithmic variants which achieve better performance than Ant System. In this paper, we present MAX – MIN Ant System ( MM AS ), an Ant Colony Optimization algorithm derived from Ant System. MM AS differs from Ant System in several important aspects, whose usefulness we demonstrate by means of an experimental study. Additionally, we relate one of the characteristics specific to MM AS — that of using a greedier search than Ant System — to results from the search space analysis of the combinatorial optimization problems attacked in this paper. Our computational results on the Traveling Salesman Problem and the Quadratic Assignment Problem show that MM AS is currently among the best performing algorithms for these problems.
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paper_title: The uci kdd archive

paper_content:

Means for vacuum forming a plastic film to form a continuous liner about the interior of a drum, using the drum shell as a mold member.
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paper_title: Simultaneous Ant Colony Optimization Algorithms for Learning Linguistic Fuzzy Rules

paper_content:

M. Galea and Q. Shen. Simultaneous ant colony optimisation algorithms for learning linguistic fuzzy rules. A. Abraham, C. Grosan and V. Ramos (Eds.), Swarm Intelligence in Data Mining, pages 75-99.
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paper_title: An Ant Colony Based System for Data Mining: Applications To Medical

paper_content:

This work describes an algorithm for rule discovery in databases called AntMiner. The objective of the algorithm is the extraction of classification rules to be applied to unseen data as a decision aid. The algorithm used to discover such rules is inspired in the behavior of a real ant colony, as well as some concepts of information theory and data mining. AntMiner was applied to medical databases to obtain classification rules.
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paper_title: Learning Bayesian Network Equivalence Classes with Ant Colony Optimization

paper_content:

Bayesian networks are a useful tool in the representation of uncertain knowledge. This paper proposes a new algorithm called ACO-E, to learn the structure of a Bayesian network. It does this by conducting a search through the space of equivalence classes of Bayesian networks using Ant Colony Optimization (ACO). To this end, two novel extensions of traditional ACO techniques are proposed and implemented. Firstly, multiple types of moves are allowed. Secondly, moves can be given in terms of indices that are not based on construction graph nodes. The results of testing show that ACO-E performs better than a greedy search and other state-of-the-art and metaheuristic algorithms whilst searching in the space of equivalence classes.
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paper_title: Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem

paper_content:

This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to TSPs. Ants cooperate using an indirect form of communication mediated by a pheromone they deposit on the edges of the TSP graph while building solutions. We study the ACS by running experiments to understand its operation. The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and we conclude comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.
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paper_title: Learning Equivalence Classes of Bayesian Networks Structures

paper_content:

Approaches to learning Bayesian networks from data typically combine a scoring function with a heuristic search procedure. Given a Bayesian network structure, many of the scoring functions derived in the literature return a score for the entire equivalence class to which the structure belongs. When using such a scoring function, it is appropriate for the heuristic search algorithm to search over equivalence classes of Bayesian networks as opposed to individual structures. We present the general formulation of a search space for which the states of the search correspond to equivalence classes of structures. Using this space, any one of a number of heuristic search algorithms can easily be applied. We compare greedy search performance in the proposed search space to greedy search performance in a search space for which the states correspond to individual Bayesian network structures.
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paper_title: Ant colony optimization for learning Bayesian networks

paper_content:

One important approach to learning Bayesian networks (BNs) from data uses a scoring metric to evaluate the fitness of any given candidate network for the data base, and applies a search procedure to explore the set of candidate networks. The most usual search methods are greedy hill climbing, either deterministic or stochastic, although other techniques have also been used. In this paper we propose a new algorithm for learning BNs based on a recently introduced metaheuristic, which has been successfully applied to solve a variety of combinatorial optimization problems: ant colony optimization (ACO). We describe all the elements necessary to tackle our learning problem using this metaheuristic, and experimentally compare the performance of our ACO-based algorithm with other algorithms used in the literature. The experimental work is carried out using three different domains: ALARM, INSURANCE and BOBLO.
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paper_title: Ant colony optimization for continuous domains

paper_content:

In this paper we present an extension of ant colony optimization (ACO) to continuous domains. We show how ACO, which was initially developed to be a metaheuristic for combinatorial optimization, can be adapted to continuous optimization without any major conceptual change to its structure. We present the general idea, implementation, and results obtained. We compare the results with those reported in the literature for other continuous optimization methods: other ant-related approaches and other metaheuristics initially developed for combinatorial optimization and later adapted to handle the continuous case. We discuss how our extended ACO compares to those algorithms, and we present some analysis of its efficiency and robustness.
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paper_title: An Extension of Ant Colony System to Continuous Optimization Problems

paper_content:

A new method for global minimization of continuous functions has been proposed based on Ant Colony Optimization. In contrast with the previous researches on continuous ant-based methods, the proposed scheme is purely pheromone-based. The algorithm has been applied to several standard test functions and the results are compared with those of two other meta-heuristics. The overall results are compatible, in good agreement and in some cases even better than the two other methods. In addition the proposed algorithm is much simpler, which is mainly due to its simpler structure. Also it has fewer control parameters, which makes the parameter settings process easier than many other methods.
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paper_title: An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training

paper_content:

Ant colony optimization (ACO) is an optimization technique that was inspired by the foraging behaviour of real ant colonies. Originally, the method was introduced for the application to discrete optimization problems. Recently we proposed a first ACO variant for continuous optimization. In this work we choose the training of feed-forward neural networks for pattern classification as a test case for this algorithm. In addition, we propose hybrid algorithm variants that incorporate short runs of classical gradient techniques such as backpropagation. For evaluating our algorithms we apply them to classification problems from the medical field, and compare the results to some basic algorithms from the literature. The results show, first, that the best of our algorithms are comparable to gradient-based algorithms for neural network training, and second, that our algorithms compare favorably with a basic genetic algorithm.
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paper_title: Classification With Ant Colony Optimization

paper_content:

Ant colony optimization (ACO) can be applied to the data mining field to extract rule-based classifiers. The aim of this paper is twofold. On the one hand, we provide an overview of previous ant-based approaches to the classification task and compare them with state-of-the-art classification techniques, such as C4.5, RIPPER, and support vector machines in a benchmark study. On the other hand, a new ant-based classification technique is proposed, named AntMiner+. The key differences between the proposed AntMiner+ and previous AntMiner versions are the usage of the better performing MAX-MIN ant system, a clearly defined and augmented environment for the ants to walk through, with the inclusion of the class variable to handle multiclass problems, and the ability to include interval rules in the rule list. Furthermore, the commonly encountered problem in ACO of setting system parameters is dealt with in an automated, dynamic manner. Our benchmarking experiments show an AntMiner+ accuracy that is superior to that obtained by the other AntMiner versions, and competitive or better than the results achieved by the compared classification techniques.
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paper_title: Handling continuous attributes in Ant Colony Classification algorithms

paper_content:

Most real-world classification problems involve continuous (real-valued) attributes, as well as, nominal (discrete) attributes. The majority of Ant Colony Optimisation (ACO) classification algorithms have the limitation of only being able to cope with nominal attributes directly. Extending the approach for coping with continuous attributes presented by cAnt-Miner (Ant-Miner coping with continuous attributes), in this paper we propose two new methods for handling continuous attributes in ACO classification algorithms. The first method allows a more flexible representation of continuous attributes' intervals. The second method explores the problem of attribute interaction, which originates from the way that continuous attributes are handled in cAnt-Miner, in order to implement an improved pheromone updating method. Empirical evaluation on eight publicly available data sets shows that the proposed methods facilitate the discovery of more accurate classification models.
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paper_title: The dynamics of collective sorting robot-like ants and ant-like robots

paper_content:

A distributed sorting algorithm, inspired by how ant colonies sort their brood. is presented for use by robot teams. The robots move randomly, do not communicate. have no hierarchical organisation, have no global representation. can only perceive objects just in front of them, but can distinguish between objects of two or more types with a certain degree of error. The probability that they pick up or put down an object is modulated as a function of how many of the same objects they have met in the recent past This generates a positive feed-back that is sufficient to coordinate the robots' activity, resulting in their sorting the objects into common clusters. While less efficient than a hierarchically controlled sorting, this decentralised organisation offers the advantages of simplicity, flexibility and robustness.
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paper_title: Data Mining Practical Machine Learning Tools And Techniques With Java Implementations

paper_content:

Thank you for reading data mining practical machine learning tools and techniques with java implementations. As you may know, people have look hundreds times for their favorite novels like this data mining practical machine learning tools and techniques with java implementations, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their laptop.
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paper_title: Ant-Based Clustering and Topographic Mapping

paper_content:

Ant-based clustering and sorting is a nature-inspired heuristic first introduced as a model for explaining two types of emergent behavior observed in real ant colonies. More recently, it has been applied in a data-mining context to perform both clustering and topographic mapping. Early work demonstrated some promising characteristics of the heuristic but did not extend to a rigorous investigation of its capabilities. We describe an improved version, called ATTA, incorporating adaptive, heterogeneous ants, a time-dependent transporting activity, and a method (for clustering applications) that transforms the spatial embedding produced by the algorithm into an explicit partitioning. ATTA is then subjected to the most rigorous experimental evaluation of an ant-based clustering and sorting algorithm undertaken to date: we compare its performance with standard techniques for clustering and topographic mapping using a set of analytical evaluation functions and a range of synthetic and real data collections. Our results demonstrate the ability of ant-based clustering and sorting to automatically identify the number of clusters inherent in a data collection, and to produce high quality solutions; indeed, we show that it is particularly robust for clusters of differing sizes and for overlapping clusters. The results obtained for topographic mapping are, however, disappointing. We provide evidence that the solutions generated by the ant algorithm are barely topology-preserving, and we explain in detail why results have—in spite of this—been misinterpreted (much more positively) in previous research.
---
paper_title: The dynamics of collective sorting robot-like ants and ant-like robots

paper_content:

A distributed sorting algorithm, inspired by how ant colonies sort their brood. is presented for use by robot teams. The robots move randomly, do not communicate. have no hierarchical organisation, have no global representation. can only perceive objects just in front of them, but can distinguish between objects of two or more types with a certain degree of error. The probability that they pick up or put down an object is modulated as a function of how many of the same objects they have met in the recent past This generates a positive feed-back that is sufficient to coordinate the robots' activity, resulting in their sorting the objects into common clusters. While less efficient than a hierarchically controlled sorting, this decentralised organisation offers the advantages of simplicity, flexibility and robustness.
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paper_title: Swarm intelligence clustering algorithm based on attractor

paper_content:

Ant colonies behavior and their self-organizing capabilities have been popularly studied, and various swarm intelligence models and clustering algorithms also have been proposed. Unfortunately, the cluster number is often too high and convergence is also slow. We put forward a novel structure-attractor, which actively attracts and guides the ant's behavior, and implement an efficient strategy to adaptively control the clustering behavior. Our experiments show that swarm intelligence clustering algorithm based on attractor (SICABA for short) greatly improves the convergence speed and clustering quality compared with LF and also has many notable virtues such as flexibility, decentralization compared with conventional algorithms.
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paper_title: Ant-Based Clustering and Topographic Mapping

paper_content:

Ant-based clustering and sorting is a nature-inspired heuristic first introduced as a model for explaining two types of emergent behavior observed in real ant colonies. More recently, it has been applied in a data-mining context to perform both clustering and topographic mapping. Early work demonstrated some promising characteristics of the heuristic but did not extend to a rigorous investigation of its capabilities. We describe an improved version, called ATTA, incorporating adaptive, heterogeneous ants, a time-dependent transporting activity, and a method (for clustering applications) that transforms the spatial embedding produced by the algorithm into an explicit partitioning. ATTA is then subjected to the most rigorous experimental evaluation of an ant-based clustering and sorting algorithm undertaken to date: we compare its performance with standard techniques for clustering and topographic mapping using a set of analytical evaluation functions and a range of synthetic and real data collections. Our results demonstrate the ability of ant-based clustering and sorting to automatically identify the number of clusters inherent in a data collection, and to produce high quality solutions; indeed, we show that it is particularly robust for clusters of differing sizes and for overlapping clusters. The results obtained for topographic mapping are, however, disappointing. We provide evidence that the solutions generated by the ant algorithm are barely topology-preserving, and we explain in detail why results have—in spite of this—been misinterpreted (much more positively) in previous research.
---
paper_title: Data Mining Practical Machine Learning Tools And Techniques With Java Implementations

paper_content:

Thank you for reading data mining practical machine learning tools and techniques with java implementations. As you may know, people have look hundreds times for their favorite novels like this data mining practical machine learning tools and techniques with java implementations, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their laptop.
---
paper_title: Particle swarm optimization

paper_content:

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
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paper_title: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions

paper_content:

This paper presents a variant of particle swarm optimizers (PSOs) that we call the comprehensive learning particle swarm optimizer (CLPSO), which uses a novel learning strategy whereby all other particles' historical best information is used to update a particle's velocity. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. Experiments were conducted (using codes available from http://www.ntu.edu.sg/home/epnsugan) on multimodal test functions such as Rosenbrock, Griewank, Rastrigin, Ackley, and Schwefel and composition functions both with and without coordinate rotation. The results demonstrate good performance of the CLPSO in solving multimodal problems when compared with eight other recent variants of the PSO.
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paper_title: A modified particle swarm optimizer

paper_content:

Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manipulated according to the rule of survival of the fittest through "genetic" operations, such as mutation, crossover and reproduction. A best solution is evolved through the generations. In contrast to evolutionary computation techniques, Eberhart and Kennedy developed a different algorithm through simulating social behavior (R.C. Eberhart et al., 1996; R.C. Eberhart and J. Kennedy, 1996; J. Kennedy and R.C. Eberhart, 1995; J. Kennedy, 1997). As in other algorithms, a population of individuals exists. This algorithm is called particle swarm optimization (PSO) since it resembles a school of flying birds. In a particle swarm optimizer, instead of using genetic operators, these individuals are "evolved" by cooperation and competition among the individuals themselves through generations. Each particle adjusts its flying according to its own flying experience and its companions' flying experience. We introduce a new parameter, called inertia weight, into the original particle swarm optimizer. Simulations have been done to illustrate the significant and effective impact of this new parameter on the particle swarm optimizer.
---
paper_title: The fully informed particle swarm: simpler, maybe better

paper_content:

The canonical particle swarm algorithm is a new approach to optimization, drawing inspiration from group behavior and the establishment of social norms. It is gaining popularity, especially because of the speed of convergence and the fact that it is easy to use. However, we feel that each individual is not simply influenced by the best performer among his neighbors. We, thus, decided to make the individuals "fully informed." The results are very promising, as informed individuals seem to find better solutions in all the benchmark functions.
---
paper_title: Particle swarm optimization

paper_content:

Particle swarm optimization (PSO) has undergone many changes since its intro- duction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors' perspective, including variations in the algorithm, current and ongoing research, applications and open problems.
---
paper_title: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients

paper_content:

This paper introduces a novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations. Initially, to efficiently control the local search and convergence to the global optimum solution, time-varying acceleration coefficients (TVAC) are introduced in addition to the time-varying inertia weight factor in particle swarm optimization (PSO). From the basis of TVAC, two new strategies are discussed to improve the performance of the PSO. First, the concept of "mutation" is introduced to the particle swarm optimization along with TVAC (MPSO-TVAC), by adding a small perturbation to a randomly selected modulus of the velocity vector of a random particle by predefined probability. Second, we introduce a novel particle swarm concept "self-organizing hierarchical particle swarm optimizer with TVAC (HPSO-TVAC)". Under this method, only the "social" part and the "cognitive" part of the particle swarm strategy are considered to estimate the new velocity of each particle and particles are reinitialized whenever they are stagnated in the search space. In addition, to overcome the difficulties of selecting an appropriate mutation step size for different problems, a time-varying mutation step size was introduced. Further, for most of the benchmarks, mutation probability is found to be insensitive to the performance of MPSO-TVAC method. On the other hand, the effect of reinitialization velocity on the performance of HPSO-TVAC method is also observed. Time-varying reinitialization step size is found to be an efficient parameter optimization strategy for HPSO-TVAC method. The HPSO-TVAC strategy outperformed all the methods considered in this investigation for most of the functions. Furthermore, it has also been observed that both the MPSO and HPSO strategies perform poorly when the acceleration coefficients are fixed at two.
---
paper_title: A discrete binary version of the particle swarm algorithm

paper_content:

The particle swarm algorithm adjusts the trajectories of a population of "particles" through a problem space on the basis of information about each particle's previous best performance and the best previous performance of its neighbors. Previous versions of the particle swarm have operated in continuous space, where trajectories are defined as changes in position on some number of dimensions. The paper reports a reworking of the algorithm to operate on discrete binary variables. In the binary version, trajectories are changes in the probability that a coordinate will take on a zero or one value. Examples, applications, and issues are discussed.
---
paper_title: On the convergence analysis and parameter selection in particle swarm optimization

paper_content:

A PSO with increasing inertia weight, distinct from a widely used PSO with decreasing inertia weight, is proposed in this paper. Far from drawing conclusions from sole empirical study or rule of thumb, this algorithm is derived from particle trajectory study and convergence analysis. Four standard test functions are used to confirm its validity finally. From the experiments, it is clear that a PSO with increasing inertia weight outperforms the one with decreasing inertia weight, both in convergent speed and solution precision, with no additional computing load.
---
paper_title: Particle Swarm based Data Mining Algorithms for classification tasks

paper_content:

Particle Swarm Optimisers are inherently distributed algorithms where the solution for a problem emerges from the interactions between many simple individual agents called particles. This article proposes the use of the Particle Swarm Optimiser as a new tool for Data Mining. In the first phase of our research, three different Particle Swarm Data Mining Algorithms were implemented and tested against a Genetic Algorithm and a Tree Induction Algorithm (J48). From the obtained results, Particle Swarm Optimisers proved to be a suitable candidate for classification tasks. The second phase was dedicated to improving one of the Particle Swarm optimiser variants in terms of attribute type support and temporal complexity. The data sources here used for experimental testing are commonly used and considered as a de facto standard for rule discovery algorithms reliability ranking. The results obtained in these domains seem to indicate that Particle Swarm Data Mining Algorithms are competitive, not only with other evolutionary techniques, but also with industry standard algorithms such as the J48 algorithm, and can be successfully applied to more demanding problem domains.
---
paper_title: Swarm Intelligence for Multi-objective Problems in Data Mining

paper_content:

The purpose of this book is to collect contributions that are at the intersection of multi-objective optimization, swarm intelligence (specifically, particle swarm optimization and ant colony optimization) and data mining. Such a collection intends to illustrate the potential of multi-objective swarm intelligence techniques in data mining, with the aim of motivating more researchers in evolutionary computation and machine learning to do research in this field. This volume consists of eleven chapters, including an introduction that provides the basic concepts of swarm intelligence techniques and a discussion of their use in data mining. Some of the research challenges that must be faced when using swarm intelligence techniques in data mining are also addressed. The rest of the chapters were contributed by leading researchers, and were organized according to the steps normally followed in Knowledge Discovery in Databases (KDD) (i.e., data preprocessing, data mining, and post processing). We hope that this book becomes a valuable reference for those wishing to do research on the use of multi-objective swarm intelligence techniques in data mining and knowledge discovery in databases.
---
paper_title: Hierarchical Classification of Protein Function with Ensembles of Rules and Particle Swarm Optimisation

paper_content:

This paper focuses on hierarchical classification problems where the classes to be predicted are organized in the form of a tree. The standard top-down divide and conquer approach for hierarchical classification consists of building a hierarchy of classifiers where a classifier is built for each internal (non-leaf) node in the class tree. Each classifier discriminates only between its child classes. After the tree of classifiers is built, the system uses them to classify test examples one class level at a time, so that when the example is assigned a class at a given level, only the child classes need to be considered at the next level. This approach has the drawback that, if a test example is misclassified at a certain class level, it will be misclassified at deeper levels too. In this paper we propose hierarchical classification methods to mitigate this drawback. More precisely, we propose a method called hierarchical ensemble of hierarchical rule sets (HEHRS), where different ensembles are built at different levels in the class tree and each ensemble consists of different rule sets built from training examples at different levels of the class tree. We also use a particle swarm optimisation (PSO) algorithm to optimise the rule weights used by HEHRS to combine the predictions of different rules into a class to be assigned to a given test example. In addition, we propose a variant of a method to mitigate the aforementioned drawback of top-down classification. These three types of methods are compared against the standard top-down hierarchical classification method in six challenging bioinformatics datasets, involving the prediction of protein function. Overall HEHRS with the rule weights optimised by the PSO algorithm obtains the best predictive accuracy out of the four types of hierarchical classification method.
---
paper_title: Application of particle swarm optimization and proximal support vector machines for fault detection

paper_content:

This paper presents a novel application of particle swarm optimization (PSO) in combination with another computational intelligence (CI) technique, namely, proximal support vector machine (PSVM) for machinery fault detection. Both real-valued and binary PSO algorithms have been considered along with linear and nonlinear versions of PSVM. The time domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The features extracted from original and preprocessed signals are used as inputs to the classifiers (PSVM) for detection of machine condition. Input features are selected using a PSO algorithm. The classifiers are trained with a subset of experimental data for known machine conditions and are tested using the remaining data. The procedure is illustrated using the experimental vibration data of a rotating machine. The influences of the number of features, PSO algorithms and type of classifiers (linear or nonlinear PSVM) on the detection success are investigated. Results are compared with a genetic algorithm (GA) and principal component analysis (PCA). The PSO based approach gave test classification success above 90% which were comparable with the GA and much better than PCA. The results show the effectiveness of the selected features and classifiers in detection of machine condition.
---
paper_title: Particle swarm optimization

paper_content:

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
---
paper_title: Data clustering using particle swarm optimization

paper_content:

This paper proposes two new approaches to using PSO to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters. The algorithm is then extended to use K-means clustering to seed the initial swarm. This second algorithm basically uses PSO to refine the clusters formed by K-means. The new PSO algorithms are evaluated on six data sets, and compared to the performance of K-means clustering. Results show that both PSO clustering techniques have much potential.
---
paper_title: Training support vector machines with particle swarms

paper_content:

Training a support vector machine requires solving a constrained quadratic programming problem. Linear particle swarm optimization is intuitive and simple to implement, and is presented as an alternative to current numeric SVM training methods. Performance of the new algorithm is demonstrated on the MNIST character recognition dataset.
---
paper_title: Particle swarm optimizer for variable weighting in clustering high-dimensional data

paper_content:

This paper proposes a particle swarm optimizer to solve the variable weighting problem in subspace clustering of high-dimensional data. Many subspace clustering algorithms fail to yield good cluster quality because they do not employ an efficient search strategy. In this paper, we are interested in soft subspace clustering and design a suitable weighting k-means objective function, on which a change of variable weights is exponentially reflected. We transform the original constrained variable weighting problem into a problem with bound constraints using a potential solution coding method and we develop a particle swarm optimizer to minimize the objective function in order to obtain global optima to the variable weighting problem in clustering. Our experimental results on synthetic datasets show that the proposed algorithm greatly improves cluster quality. In addition, the result of the new algorithm is much less dependent on the initial cluster centroids.
---
paper_title: Locally adaptive metrics for clustering high dimensional data

paper_content:

Clustering suffers from the curse of dimensionality, and similarity functions that use all input features with equal relevance may not be effective. We introduce an algorithm that discovers clusters in subspaces spanned by different combinations of dimensions via local weightings of features. This approach avoids the risk of loss of information encountered in global dimensionality reduction techniques, and does not assume any data distribution model. Our method associates to each cluster a weight vector, whose values capture the relevance of features within the corresponding cluster. We experimentally demonstrate the gain in perfomance our method achieves with respect to competitive methods, using both synthetic and real datasets. In particular, our results show the feasibility of the proposed technique to perform simultaneous clustering of genes and conditions in gene expression data, and clustering of very high-dimensional data such as text data.
---
paper_title: Foraging theory for dimensionality reduction of clustered data

paper_content:

We present a bioinspired algorithm which performs dimensionality reduction on datasets for visual exploration, under the assumption that they have a clustered structure. We formulate a decision-making strategy based on foraging theory, where a software agent is viewed as an animal, a discrete space as the foraging landscape, and objects representing points from the dataset as nutrients or prey items. We apply this algorithm to artificial and real databases, and show how a multi-agent system addresses the problem of mapping high-dimensional data into a two-dimensional space.
---
paper_title: Ant-Based Clustering and Topographic Mapping

paper_content:

Ant-based clustering and sorting is a nature-inspired heuristic first introduced as a model for explaining two types of emergent behavior observed in real ant colonies. More recently, it has been applied in a data-mining context to perform both clustering and topographic mapping. Early work demonstrated some promising characteristics of the heuristic but did not extend to a rigorous investigation of its capabilities. We describe an improved version, called ATTA, incorporating adaptive, heterogeneous ants, a time-dependent transporting activity, and a method (for clustering applications) that transforms the spatial embedding produced by the algorithm into an explicit partitioning. ATTA is then subjected to the most rigorous experimental evaluation of an ant-based clustering and sorting algorithm undertaken to date: we compare its performance with standard techniques for clustering and topographic mapping using a set of analytical evaluation functions and a range of synthetic and real data collections. Our results demonstrate the ability of ant-based clustering and sorting to automatically identify the number of clusters inherent in a data collection, and to produce high quality solutions; indeed, we show that it is particularly robust for clusters of differing sizes and for overlapping clusters. The results obtained for topographic mapping are, however, disappointing. We provide evidence that the solutions generated by the ant algorithm are barely topology-preserving, and we explain in detail why results have—in spite of this—been misinterpreted (much more positively) in previous research.
---
paper_title: A Global Geometric Framework for Nonlinear Dimensionality Reduction

paper_content:

Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly confront the problem of dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. The human brain confronts the same problem in everyday perception, extracting from its high-dimensional sensory inputs—30,000 auditory nerve fibers or 106 optic nerve fibers—a manageably small number of perceptually relevant features. Here we describe an approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set. Unlike classical techniques such as principal component analysis (PCA) and multidimensional scaling (MDS), our approach is capable of discovering the nonlinear degrees of freedom that underlie complex natural observations, such as human handwriting or images of a face under different viewing conditions. In contrast to previous algorithms for nonlinear dimensionality reduction, ours efficiently computes a globally optimal solution, and, for an important class of data manifolds, is guaranteed to converge asymptotically to the true structure.
---
paper_title: Step-Wise Clustering Procedures

paper_content:

Abstract A simple step-wise procedure for the clustering of variables is described. Two alternative criteria for the merger of groups at each pass are discussed: (1) maximization of the pairwise correlation between the centroids of two groups, and (2) minimization of Wilks’ statistic to test the hypothesis of independence between two groups. For a set of sample covariance matrices the step-wise solution for each criterion is compared with the optimal two-group separation of variables found by total enumeration of the possible groupings.
---
paper_title: Foraging theory for dimensionality reduction of clustered data

paper_content:

We present a bioinspired algorithm which performs dimensionality reduction on datasets for visual exploration, under the assumption that they have a clustered structure. We formulate a decision-making strategy based on foraging theory, where a software agent is viewed as an animal, a discrete space as the foraging landscape, and objects representing points from the dataset as nutrients or prey items. We apply this algorithm to artificial and real databases, and show how a multi-agent system addresses the problem of mapping high-dimensional data into a two-dimensional space.
---
paper_title: On the quest for optimal rule learning heuristics

paper_content:

The primary goal of the research reported in this paper is to identify what criteria are responsible for the good performance of a heuristic rule evaluation function in a greedy top-down covering algorithm. We first argue that search heuristics for inductive rule learning algorithms typically trade off consistency and coverage, and we investigate this trade-off by determining optimal parameter settings for five different parametrized heuristics. In order to avoid biasing our study by known functional families, we also investigate the potential of using metalearning for obtaining alternative rule learning heuristics. The key results of this experimental study are not only practical default values for commonly used heuristics and a broad comparative evaluation of known and novel rule learning heuristics, but we also gain theoretical insights into factors that are responsible for a good performance. For example, we observe that consistency should be weighted more heavily than coverage, presumably because a lack of coverage can later be corrected by learning additional rules.
---
paper_title: Particle swarm optimization

paper_content:

Particle swarm optimization (PSO) has undergone many changes since its intro- duction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors' perspective, including variations in the algorithm, current and ongoing research, applications and open problems.
---
paper_title: Is the UCI Repository Useful for Data Mining

paper_content:

We propose a methodology to investigate the relevance for the real world of repositories of benchmark problems like the one commonly known as the UCI repository. It compares the distribution of relative performance of algorithms in data sets from a given repository and from the “real world”. If the distributions are different, the knowledge about the relative performance of algorithms obtained from the repository in question is mostly useless. In the case of the UCI repository, this would mean that a significant proportion of published results would be of little practical use. However, this is not what our results indicate. We also propose an adaptation of this method to test whether tool developers are “overfitting” repositories, which also yields negative results in the UCI repository.
---
paper_title: Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior

paper_content:

Nature-inspired optimization algorithms, notably evolutionary algorithms (EAs), have been widely used to solve various scientific and engineering problems because of to their simplicity and flexibility. Here we report a novel optimization algorithm, group search optimizer (GSO), which is inspired by animal behavior, especially animal searching behavior. The framework is mainly based on the producer-scrounger model, which assumes that group members search either for ldquofindingrdquo (producer) or for ldquojoiningrdquo (scrounger) opportunities. Based on this framework, concepts from animal searching behavior, e.g., animal scanning mechanisms, are employed metaphorically to design optimum searching strategies for solving continuous optimization problems. When tested against benchmark functions, in low and high dimensions, the GSO algorithm has competitive performance to other EAs in terms of accuracy and convergence speed, especially on high-dimensional multimodal problems. The GSO algorithm is also applied to train artificial neural networks. The promising results on three real-world benchmark problems show the applicability of GSO for problem solving.
---
paper_title: GSA: A Gravitational Search Algorithm

paper_content:

In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions.
---
paper_title: Data clustering using particle swarm optimization

paper_content:

This paper proposes two new approaches to using PSO to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters. The algorithm is then extended to use K-means clustering to seed the initial swarm. This second algorithm basically uses PSO to refine the clusters formed by K-means. The new PSO algorithms are evaluated on six data sets, and compared to the performance of K-means clustering. Results show that both PSO clustering techniques have much potential.
---
paper_title: Ant-based approach to the knowledge fusion problem

paper_content:

Data mining involves the automated process of finding patterns in data and has been a research topic for decades. Although very powerful data mining techniques exist to extract classification models from data, the techniques often infer counter-intuitive patterns or lack patterns that are logical for domain experts. The problem of consolidating the knowledge extracted from the data with the knowledge representing the experience of domain experts, is called the knowledge fusion problem. Providing a proper solution for this problem is a key success factor for any data mining application. In this paper, we explain how the AntMiner+ classification technique can be extended to incorporate such domain knowledge. By changing the environment and influencing the heuristic values, we can respectively limit and direct the search of the ants to those regions of the solution space that the expert believes to be logical and intuitive.
---
paper_title: AntNet: Distributed Stigmergetic Control for Communications Networks

paper_content:

This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.
---
paper_title: Privacy-preserving data mining

paper_content:

A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from training data in which the values of individual records have been perturbed. The resulting data records look very different from the original records and the distribution of data values is also very different from the original distribution. While it is not possible to accurately estimate original values in individual data records, we propose a novel reconstruction procedure to accurately estimate the distribution of original data values. By using these reconstructed distributions, we are able to build classifiers whose accuracy is comparable to the accuracy of classifiers built with the original data.
---
paper_title: Network-based marketing: Identifying likely adopters via consumer networks

paper_content:

Network-based marketing refers to a collection of marketing techniques that take advantage of links between consumers to increase sales. We concentrate on the consumer networks formed using direct interactions (e.g., communications) between consumers. We survey the diverse literature on such marketing with an emphasis on the statistical methods used and the data to which these methods have been applied. We also provide a discussion of challenges and opportunities for this burgeoning research topic. Our survey highlights a gap in the literature. Because of inadequate data, prior studies have not been able to provide direct, statistical support for the hypothesis that network linkage can directly affect product/service adoption. Using a new data set that represents the adoption of a new telecommunications service, we show very strong support for the hypothesis. Specifically, we show three main results: (1) ``Network neighbors''--those consumers linked to a prior customer--adopt the service at a rate 3--5 times greater than baseline groups selected by the best practices of the firm's marketing team. In addition, analyzing the network allows the firm to acquire new customers who otherwise would have fallen through the cracks, because they would not have been identified based on traditional attributes. (2) Statistical models, built with a very large amount of geographic, demographic and prior purchase data, are significantly and substantially improved by including network information. (3) More detailed network information allows the ranking of the network neighbors so as to permit the selection of small sets of individuals with very high probabilities of adoption.
---
paper_title: AntNet: Distributed Stigmergetic Control for Communications Networks

paper_content:

This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.
---
paper_title: Real time data mining-based intrusion detection

paper_content:

We present an overview of our research in real time data mining-based intrusion detection systems (IDSs). We focus on issues related to deploying a data mining-based IDS in a real time environment. We describe our approaches to address three types of issues: accuracy, efficiency, and usability. To improve accuracy, data mining programs are used to analyze audit data and extract features that can distinguish normal activities from intrusions; we use artificial anomalies along with normal and/or intrusion data to produce more effective misuse and anomaly detection models. To improve efficiency, the computational costs of features are analyzed and a multiple-model cost-based approach is used to produce detection models with low cost and high accuracy. We also present a distributed architecture for evaluating cost-sensitive models in real-time. To improve usability, adaptive learning algorithms are used to facilitate model construction and incremental updates; unsupervised anomaly detection algorithms are used to reduce the reliance on labeled data. We also present an architecture consisting of sensors, detectors, a data warehouse, and model generation components. This architecture facilitates the sharing and storage of audit data and the distribution of new or updated models. This architecture also improves the efficiency and scalability of the IDS.
---

Title: Editorial survey: swarm intelligence for data mining

Section 1: Swarm Intelligence
Description 1: This section explains the collective behavior of systems composed of many individuals interacting locally with each other and their environment, and details some natural examples and their computational applications.

Section 2: Swarm Intelligence for Data Mining
Description 2: This section discusses the development of numerous data mining algorithms inspired by swarm intelligence and introduces a unifying framework for these techniques, categorizing them into effective search and data organizing approaches.

Section 3: Artificial Ant Colonies and Ant Colony Optimization
Description 3: This section elaborates on artificial ant systems and the Ant Colony Optimization (ACO) metaheuristic, outlining the general steps and specific implementations in solving optimization problems such as the traveling salesman problem.

Section 4: Data Mining Applications of ACO
Description 4: This section examines the use of ACO in data mining tasks, mainly focusing on supervised classification, and discusses the AntMiner algorithm and its derivatives.

Section 5: Clustering with ACO
Description 5: This section explores the ACO-based clustering techniques, detailing the algorithmic steps and improvements over classical clustering methods like k-means.

Section 6: Challenges for ACO-based Data Mining
Description 6: This section outlines the challenges faced by ACO-based techniques in data mining, such as discretization of variables and computational time, and suggests potential solutions and future research directions.

Section 7: Particle Swarm Optimization
Description 7: This section introduces Particle Swarm Optimization (PSO), describing its principles, algorithmic framework, and the various enhancements designed to improve its performance.

Section 8: Popular Data Mining Applications of PSO
Description 8: This section details PSO applications in data mining, such as rule-based classification models and clustering, highlighting specific implementations and their performance metrics.

Section 9: Challenges for PSO-based Data Mining
Description 9: This section discusses the challenges in PSO-based data mining, focusing on the need for systematic investigation, performance evaluation, and comparative studies with ACO.

Section 10: Foraging Agents Maximizing Energy Intake
Description 10: This section examines the foraging prey model and its application in dimensionality reduction, presenting its effectiveness in visual exploration of high-dimensional data.

Section 11: Challenges for Prey Model-Based Dimensionality Reduction
Description 11: This section discusses the limited research conducted on prey models, their similarities with ant-based sorting techniques, and the need for further investigation and benchmarking studies.

Section 12: Directions for Future Research
Description 12: This section proposes future research directions, emphasizing the need for systematic studies, real-life applications, open-source implementations, application to other data mining tasks, combining approaches, and incorporating domain knowledge.