A Survey Of Clustering Algorithms In Association Rules Mining
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paper_title: An efficient cluster and decomposition algorithm for mining association rules

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Conventional algorithms for mining association rules operate in a combination of smaller large itemsets. This paper presents a new efficient which combines both the cluster concept and decomposition of larger candidate itemsets, while proceeds from mining the maximal large itemsets down to large 1-itemsets, named cluster-decomposition association rule (CDAR). First, the CDAR method creates some clusters by reading the database only once, and then clustering the transaction records to the kth cluster, where the length of a record is k. Then, the large k-itemsets are generated by contrasts with the kth cluster only, unlike the combination concept that contrasts with the entire database. Experiments with real-life databases show that CDAR outperforms Apriori, a well-known and widely used association rule.
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paper_title: Frequent pattern mining: current status and future directions

paper_content:

Frequent pattern mining has been a focused theme in data mining research for over a decade. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications. In this article, we provide a brief overview of the current status of frequent pattern mining and discuss a few promising research directions. We believe that frequent pattern mining research has substantially broadened the scope of data analysis and will have deep impact on data mining methodologies and applications in the long run. However, there are still some challenging research issues that need to be solved before frequent pattern mining can claim a cornerstone approach in data mining applications.
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paper_title: Cluster center initialization algorithm for K-means clustering

paper_content:

Performance of iterative clustering algorithms which converges to numerous local minima depend highly on initial cluster centers. Generally initial cluster centers are selected randomly. In this paper we propose an algorithm to compute initial cluster centers for K-means clustering. This algorithm is based on two observations that some of the patterns are very similar to each other and that is why they have same cluster membership irrespective to the choice of initial cluster centers. Also, an individual attribute may provide some information about initial cluster center. The initial cluster centers computed using this methodology are found to be very close to the desired cluster centers, for iterative clustering algorithms. This procedure is applicable to clustering algorithms for continuous data. We demonstrate the application of proposed algorithm to K-means clustering algorithm. The experimental results show improved and consistent solutions using the proposed algorithm.
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paper_title: A possibilistic fuzzy c-means clustering algorithm

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In 1997, we proposed the fuzzy-possibilistic c-means (FPCM) model and algorithm that generated both membership and typicality values when clustering unlabeled data. FPCM constrains the typicality values so that the sum over all data points of typicalities to a cluster is one. The row sum constraint produces unrealistic typicality values for large data sets. In this paper, we propose a new model called possibilistic-fuzzy c-means (PFCM) model. PFCM produces memberships and possibilities simultaneously, along with the usual point prototypes or cluster centers for each cluster. PFCM is a hybridization of possibilistic c-means (PCM) and fuzzy c-means (FCM) that often avoids various problems of PCM, FCM and FPCM. PFCM solves the noise sensitivity defect of FCM, overcomes the coincident clusters problem of PCM and eliminates the row sum constraints of FPCM. We derive the first-order necessary conditions for extrema of the PFCM objective function, and use them as the basis for a standard alternating optimization approach to finding local minima of the PFCM objective functional. Several numerical examples are given that compare FCM and PCM to PFCM. Our examples show that PFCM compares favorably to both of the previous models. Since PFCM prototypes are less sensitive to outliers and can avoid coincident clusters, PFCM is a strong candidate for fuzzy rule-based system identification.
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paper_title: Semantic Clustering of Functional Requirements Using Agglomerative Hierarchical Clustering

paper_content:

Software applications have become a fundamental part in the daily work of modern society as they meet different needs of users in different domains. Such needs are known as software requirements (SRs) which are separated into functional (software services) and non-functional (quality attributes). The first step of every software development project is SR elicitation. This step is a challenge task for developers as they need to understand and analyze SRs manually. For example, the collected functional SRs need to be categorized into different clusters to break-down the project into a set of sub-projects with related SRs and devote each sub-project to a separate development team. However, functional SRs clustering has never been considered in the literature. Therefore, in this paper, we propose an approach to automatically cluster functional requirements based on semantic measure. An empirical evaluation is conducted using four open-access software projects to evaluate our proposal. The experimental results demonstrate that the proposed approach identifies semantic clusters according to well-known used measures in the subject.
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paper_title: Exploring expression data: identification and analysis of coexpressed genes.

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Analysis procedures are needed to extract useful information from the large amount of gene expression data that is becoming available. This work describes a set of analytical tools and their application to yeast cell cycle data. The components of our approach are (1) a similarity measure that reduces the number of false positives, (2) a new clustering algorithm designed specifically for grouping gene expression patterns, and (3) an interactive graphical cluster analysis tool that allows user feedback and validation. We use the clusters generated by our algorithm to summarize genome-wide expression and to initiate supervised clustering of genes into biologically meaningful groups.
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paper_title: Clustering algorithms based on minimum and maximum spanning trees

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We consider clustering problems under two different optimization criteria. One is to minimize the maximum intracluster distance (diameter), and the other is to maximize the minimum intercluster distance. In particular, we present an algorithm which partitions a set S of n points in the plane into two subsets so that their larger diameter is minimized in time O ( n log n ) and space O ( n ). Another algorithm with the same bounds computes a k -partition of S for any k so that the minimum intercluster distance is maximized. In both instances it is first shown that an optimal parition is determined by either a maximum or minimum spanning tree of S .
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paper_title: Clustering by fast search and find of density peaks

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Cluster analysis is aimed at classifying elements into categories on the basis of their similarity. Its applications range from astronomy to bioinformatics, bibliometrics, and pattern recognition. We propose an approach based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities. This idea forms the basis of a clustering procedure in which the number of clusters arises intuitively, outliers are automatically spotted and excluded from the analysis, and clusters are recognized regardless of their shape and of the dimensionality of the space in which they are embedded. We demonstrate the power of the algorithm on several test cases.
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Title: A Survey Of Clustering Algorithms In Association Rules Mining

Section 1: INTRODUCTION
Description 1: This section introduces the concept of clustering, its role in data modeling, and its applications in data mining, specifically focusing on clustering algorithms rather than clustering techniques.

Section 2: ASSOCIATION RULES MINING
Description 2: This section explains the process of association rules mining, emphasizing the efficiency and steps involved in generating frequent itemsets and extracting strong association rules from them.

Section 3: IMPROVEMENTS OF FREQUENT ITEMSET GENERATION
Description 3: This section discusses methods to enhance the generation of frequent itemsets, including techniques like data pruning, reducing transaction numbers, and utilizing efficient data structures.

Section 4: REQUIREMENTS OF CLUSTERING IN MINING ASSOCIATION RULES
Description 4: This section lists and explains eight different requirements for an efficient clustering process in association rule mining, highlighting the essential characteristics and capabilities needed in clustering algorithms.

Section 5: UNSUPERVISED LINEAR CLUSTERING ALGORITHMS
Description 5: This section describes the main unsupervised linear clustering algorithms, such as K-means, fuzzy c-means, hierarchical clustering, Gaussian (EM) clustering, and Quality Threshold clustering, detailing their methodologies and distinct features.

Section 6: K-MEANS CLUSTERING ALGORITHM
Description 6: This section provides a detailed description of the K-means clustering algorithm, including its steps, goals, and the main objective of minimizing the mean squared error (MSE).

Section 7: FUZZY C-MEANS CLUSTERING ALGORITHM
Description 7: This section elaborates on the fuzzy c-means clustering algorithm, explaining how membership and cluster centers are updated based on the distance between data points and cluster centers.

Section 8: HIERARCHICAL CLUSTERING ALGORITHM
Description 8: This section discusses the two main kinds of hierarchical clustering algorithms—Agglomerative and Divisive—and explains one of them in detail to illustrate the concept.

Section 9: GAUSSIAN (EXPECTATION MAXIMIZATION EM) CLUSTERING ALGORITHM
Description 9: This section outlines the Gaussian (EM) clustering algorithm, including its process of fitting data points into Gaussian distributions and highlighting its advantages and complexity.

Section 10: QUALITY THRESHOLD (QT) CLUSTERING ALGORITHM
Description 10: This section explains the QT clustering algorithm, including the identification of threshold distances and the formation of clusters based on candidate data points within these thresholds.

Section 11: UNSUPERVISED LINEAR CLUSTERING ALGORITHMS SUMMARY
Description 11: This section summarizes the advantages and disadvantages of the unsupervised linear clustering algorithms discussed, providing a comparative insight into their performance and complexity.

Section 12: UNSUPERVISED NON-LINEAR CLUSTERING ALGORITHMS
Description 12: This section introduces the main unsupervised non-linear clustering algorithms, such as MST-based clustering, Kernel k-means clustering, and Density based clustering, describing their unique methodologies.

Section 13: MINIMUM SPANNING TREE (MST) BASED CLUSTERING ALGORITHM
Description 13: This section details the MST-based clustering algorithm, describing its process of constructing a minimum spanning tree and forming clusters by removing edges based on threshold values.

Section 14: KERNEL K-MEANS CLUSTERING ALGORITHM
Description 14: This section distinguishes Kernel k-means clustering from the standard k-means by explaining the use of kernel methods to calculate distances instead of Euclidean distance.

Section 15: DENSITY BASED CLUSTERING
Description 15: This section focuses on density-based clustering algorithms like DBSCAN, detailing concepts like density reachability and connectivity for identifying clusters in data with non-linear shapes.

Section 16: SUMMARY
Description 16: This section concludes the paper, summarizing and comparing the discussed cluster-based algorithms and techniques, and suggesting future work to study the clustering algorithms in terms of their efficiency, usability, and flexibility.