A Survey on optimization approaches to text document clustering
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paper_title: Combination of Fuzzy C-means and Harmony Search Algorithms for Clustering of Text Document

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Document clustering, an important tool for document organization and browsing, has become an active research field in the machine learning community. Fuzzy c-means, an unsupervised clustering algorithm, has been widely used for categorization problems. However, as an optimization algorithm, it easily leads to local optimal clusters. To overcome this shortcoming, this paper introduces a hybrid approach of which combines the fuzzy c-means and harmony search algorithms for clustering of text document. First, we utilize harmony search algorithm to find near global optimal clusters. Then, we combine fuzzy c-means with harmony search algorithm to achieve better clustering. Experimental results on two commonly used data sets demonstrate the effectiveness and utility of this new approach.
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paper_title: Evaluation of text document clustering approach based on particle swarm optimization

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Abstract Clustering, an extremely important technique in Data Mining is an automatic learning technique aimed at grouping a set of objects into subsets or clusters. The goal is to create clusters that are coherent internally, but substantially different from each other. Text Document Clustering refers to the clustering of related text documents into groups based upon their content. It is a fundamental operation used in unsupervised document organization, text data mining, automatic topic extraction, and information retrieval. Fast and high-quality document clustering algorithms play an important role in effectively navigating, summarizing, and organizing information. The documents to be clustered can be web news articles, abstracts of research papers etc. This paper proposes two techniques for efficient document clustering involving the application of soft computing approach as an intelligent hybrid approach PSO algorithm. The proposed approach involves partitioning Fuzzy C-Means algorithm and K-Means algorithm each hybridized with Particle Swarm Optimization (PSO). The performance of these hybrid algorithms has been evaluated against traditional partitioning techniques (K-Means and Fuzzy C Means).
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paper_title: Data clustering using particle swarm optimization

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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.
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paper_title: FCM: The fuzzy c-means clustering algorithm

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Abstract This paper transmits a FORTRAN-IV coding of the fuzzy c -means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering criterion used to aggregate subsets is a generalized least-squares objective function. Features of this program include a choice of three norms (Euclidean, Diagonal, or Mahalonobis), an adjustable weighting factor that essentially controls sensitivity to noise, acceptance of variable numbers of clusters, and outputs that include several measures of cluster validity.
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paper_title: Data clustering: a review

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Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overviewof pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.
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paper_title: BIRCH: an efficient data clustering method for very large databases

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Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely studied problems in this area is the identification of clusters, or densely populated regions, in a multi-dimensional dataset. Prior work does not adequately address the problem of large datasets and minimization of I/O costs.This paper presents a data clustering method named BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies), and demonstrates that it is especially suitable for very large databases. BIRCH incrementally and dynamically clusters incoming multi-dimensional metric data points to try to produce the best quality clustering with the available resources (i.e., available memory and time constraints). BIRCH can typically find a good clustering with a single scan of the data, and improve the quality further with a few additional scans. BIRCH is also the first clustering algorithm proposed in the database area to handle "noise" (data points that are not part of the underlying pattern) effectively.We evaluate BIRCH's time/space efficiency, data input order sensitivity, and clustering quality through several experiments. We also present a performance comparisons of BIRCH versus CLARANS, a clustering method proposed recently for large datasets, and show that BIRCH is consistently superior.
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paper_title: Hybrid PSO and GA models for Document Clustering

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This paper presents Hybrid Particle Swarm Optimization (PSO) Genetic Algorithm (GA) approaches for the document clustering problem. To obtain an optimal solution using Genetic Algorithm, operation such as selection, reproduction, and mutation procedures are used to generate for the next generations. In this case, it is possible to obtain local solution because chromosomes or individuals which have only a close similarity can converge. In standard PSO the non-oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on suboptimal solutions that are not even guaranteed to local optimal solution. This work proposes hybrid models that enhance the search process by applying GA operations on stagnated particles and chromosomes. GA will be combined with PSO for improving the diversity, and the convergence toward the preferred solution for the document clustering problem. The approach efficiency is verified and tested using a set of document corpus. Our results indicate that the approaches are feasible alternative to solve document clustering problems.
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paper_title: Principles of soft computing

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The CD contains the following content. ::: 1. Power point presentations ::: * Presentations are given for Chapters 1–17, 19. ::: * MATLAB Soft Computing tools presentations are also included for easy reference of the readers to know the basic commands. ::: 2. Source Codes for Soft Computing Techniques in C ::: * Source codes are given for all the problems solved in Chapter 18. ::: * The programs are as *.txt files. ::: 3. MATLAB Source code programs ::: * MATLAB Source codes are given for problems solved in Chapter 19. ::: * The program files are given as per their problem numbers in their respective chapters. ::: 4. Copyright page ::: ::: Do install the required software before running the programs given.
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paper_title: An improved GA and a novel PSO-GA-based hybrid algorithm

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Inspired by the natural features of the variable size of the population, we present a variable population-size genetic algorithm (VPGA) by introducing the ''dying probability'' for the individuals and the ''war/disease process'' for the population. Based on the VPGA and the particle swarm optimization (PSO) algorithms, a novel PSO-GA-based hybrid algorithm (PGHA) is also proposed in this paper. Simulation results show that both VPGA and PGHA are effective for the optimization problems.
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paper_title: The Bees Algorithm - A Novel Tool for Complex Optimisation Problems

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Publisher Summary This chapter presents a new population-based search algorithm called the Bees Algorithm (BA). This algorithm mimics the food foraging behavior of swarms of honeybees. In its basic version, the algorithm performs a kind of neighborhood search combined with random search and can be used for both combinatorial optimization and functional optimization. This chapter focuses on the latter. Following a description of the algorithm, the chapter presents the results obtained for a number of benchmark problems demonstrating the efficiency and robustness of the new algorithm. The results show that the algorithm can reliably handle complex multi-model optimization problems without being trapped at local solutions. One of the drawbacks of the algorithm is the number of tunable parameters used. However, it is possible to set the parameter values by conducting a small number of trials.
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paper_title: Ants for Document Clustering

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The usage of computers for mass storage has become mandatory nowadays due to World Wide Web (WWW). This has placed many challenges to the Information Retrieval (IR) system. Clustering of documents available improves the efficiency of IR system. The problem of clustering has become a combinatorial optimization problem in IR system due to the exponential growth in information over WWW. In this paper, a hybrid algorithm that combines the basic Ant Colony Optimization with Tabu search has been proposed. The feasibility of the proposed algorithm is tested over a few standard benchmark datasets. The experimental results reveal that the proposed algorithm yields promising quality clusters compared to other ones produced by K-means algorithm.
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paper_title: Modern Information Retrieval

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From the Publisher: ::: This is a rigorous and complete textbook for a first course on information retrieval from the computer science (as opposed to a user-centred) perspective. The advent of the Internet and the enormous increase in volume of electronically stored information generally has led to substantial work on IR from the computer science perspective - this book provides an up-to-date student oriented treatment of the subject.
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paper_title: A vector space model for automatic indexing

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In a document retrieval, or other pattern matching environment where stored entities (documents) are compared with each other or with incoming patterns (search requests), it appears that the best indexing (property) space is one where each entity lies as far away from the others as possible; in these circumstances the value of an indexing system may be expressible as a function of the density of the object space; in particular, retrieval performance may correlate inversely with space density. An approach based on space density computations is used to choose an optimum indexing vocabulary for a collection of documents. Typical evaluation results are shown, demonstating the usefulness of the model.
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paper_title: Fuzzy logic, neural networks, and soft computing

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In retrospect, the year 1990 may well be viewed as the beginning of a new trend in the design of household appliances, consumer electronics, cameras, and other types of widely used consumer products. The trend in question relates to a marked increase in what might be called the Machine Intelligence Quotient (MIQ) of such products compared to what it was before 1990. Today, we have microwave ovens and washing machines that can figure out on their own what settings to use to perform their tasks optimally; cameras that come close to professional photographers in picture-taking ability; and many other products that manifest an impressive capability to reason, make intelligent decisions, and learn from experience
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paper_title: Hybrid PSO and GA models for Document Clustering

paper_content:

This paper presents Hybrid Particle Swarm Optimization (PSO) Genetic Algorithm (GA) approaches for the document clustering problem. To obtain an optimal solution using Genetic Algorithm, operation such as selection, reproduction, and mutation procedures are used to generate for the next generations. In this case, it is possible to obtain local solution because chromosomes or individuals which have only a close similarity can converge. In standard PSO the non-oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on suboptimal solutions that are not even guaranteed to local optimal solution. This work proposes hybrid models that enhance the search process by applying GA operations on stagnated particles and chromosomes. GA will be combined with PSO for improving the diversity, and the convergence toward the preferred solution for the document clustering problem. The approach efficiency is verified and tested using a set of document corpus. Our results indicate that the approaches are feasible alternative to solve document clustering problems.
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paper_title: A Novel Document Clustering Algorithm Based On Ant Colony Optimization Algorithm

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Document clustering based on ant colony optimization algorithm has lately attracted the attention of many scholars throughout the globe. The aim of document clustering is to place similar content in one group, and non-similar contents in separate groups. In this article, by changing the behavior model of ant movement, we attempt to upgrade the standard ant’s clustering algorithm. Ants’ movement is completely random in the standard clustering algorithm. On the one hand, we improve the algorithm’s efficiency by making ant movements purposeful, and on the other hand, by changing the rules of ant movement, we provide conditions so that the carrier ant moves to a location with intensive similarity with the carried component, and the noncarrier ant moves to a location where a component is surrounded by dissimilar components. We tested our proposed algorithm on a set of documents extracted from the 21578 Reuters Information Bank. Results show that the proposed algorithm on presents a better average performance compared to the standard ants clustering algorithm, and the K-means algorithm.
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paper_title: Text clustering on latent semantic indexing with particle swarm optimization ( PSO ) algorithm

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Most of web users use various search engines to get specific information. A key factor in the success of web search engines are their ability to rapidly find good quality results to the queries that are based on specific terms. This paper aims at retrieving more relevant documents from a huge corpus based on the required information. We propose a particle swarm optimization algorithm based on latent semantic indexing (PSO+LSI) for text clustering. PSO family of bio-inspired algorithms has recently successfully been applied to a number of real word clustering problems. We use an adaptive inertia weight (AIW) that do proper exploration and exploitation in search space. PSO can merge with LSI to achieve best clustering accuracy and efficiency. This framework provides more relevant documents to the user and reduces the irrelevant documents. It would be seen that for all numbers of dimensions, PSO+LSI are faster than PSO+Kmeans algorithms using vector space model (VSM). It takes 22.3 s for PSO+LSI method with 1000 terms to obtain its best performance on 150 dimensions.
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Title: A Survey on Optimization Approaches to Text Document Clustering

Section 1: INTRODUCTION
Description 1: Provide an overview of the importance of document clustering, challenges faced, and different clustering methodologies.

Section 2: SOFT COMPUTING TECHNIQUES
Description 2: Explain what soft computing is and its relevance to text document clustering, including a brief discussion of various soft computing techniques.

Section 3: Genetic Algorithm (GA)
Description 3: Discuss the Genetic Algorithm in text document clustering, detailing its operators like crossover, mutation, and selection.

Section 4: Bees Algorithm (BA)
Description 4: Provide an overview of the Bees Algorithm, its inspiration from natural foraging behavior, and its application to document clustering.

Section 5: Particle Swarm Optimization (PSO)
Description 5: Explain the Particle Swarm Optimization technique, its principles, and its application in clustering, including defining pbest and gbest.

Section 6: Ant Colony Optimization (ACO)
Description 6: Describe the Ant Colony Optimization algorithm, its inspiration from natural ant behavior, and its usage in document clustering.

Section 7: DOCUMENT CLUSTERING
Description 7: Discuss the process of document clustering and various steps involved, such as preprocessing, encoding, and evaluation.

Section 8: Preprocessing
Description 8: Explain the preprocessing steps involved in document clustering, including the removal of stop words and stemming.

Section 9: Text Document Encoding
Description 9: Describe how text documents are encoded into document term matrices (DTM) and the importance of tf-idf weighting.

Section 10: Dimension reduction techniques
Description 10: Discuss the need for dimension reduction in document clustering and describe techniques like feature selection and feature extraction.

Section 11: Latent Semantic Indexing (LSI)
Description 11: Provide an overview of Latent Semantic Indexing (LSI), its use of Singular Value Decomposition (SVD), and its benefits in text processing.

Section 12: Similarity Measurement
Description 12: Describe different methods for computing similarity or dissimilarity between documents, with a focus on distance measures.

Section 13: Evaluation of Text Clustering
Description 13: Outline the metrics used to evaluate the quality of clustering, such as F-measure, Purity, and Entropy.

Section 14: Datasets
Description 14: Introduce different datasets commonly used for evaluating document clustering algorithms, like 20NewsGroup and Hamshahri.

Section 15: RELATED WORKS
Description 15: Summarize previous research efforts in text document clustering, especially those involving soft computing techniques.

Section 16: CONCLUSION
Description 16: Conclude with a summary of the survey's findings and suggest areas for future research.