Data clustering : theory, algorithms, and applications

Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. This book starts with basic information on cluster analysis, including the classification of data and the corresponding similarity measures, followed by the presentation of over 50 clustering algorithms in groups according to some specific baseline methodologies such as hierarchical, center-based, and search-based methods. As a result, readers and users can easily identify an appropriate algorithm for their applications and compare novel ideas with existing results.

The book also provides examples of clustering applications to illustrate the advantages and shortcomings of different clustering architectures and algorithms. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology, and marketing. Readers also learn how to perform cluster analysis with the C/C++ and MATLAB#65533; programming languages.

Preface

Part I Clustering, Data and Similarity Measures:

1 Data clustering

2 DataTypes

3 Scale conversion

4 Data standardization and transformation

5 Data visualization

6 Similarity and dissimilarity measures

Part II Clustering Algorithms:

7 Hierarchical clustering techniques

8 Fuzzy clustering algorithms

9 Center Based Clustering Algorithms

10 Search based clustering algorithms

11 Graph based clustering algorithms

12 Grid based clustering algorithms

13 Density based clustering algorithms

14 Model based clustering algorithms

15 Subspace clustering

16 Miscellaneous algorithms

17 Evaluation of clustering algorithms

Part III Applications of Clustering:

18 Clustering gene expression data

Part IV Matlab and C++ for Clustering:

19 Data clustering in Matlab

20 Clustering in C/C++

A Some clustering algorithms

B Thekd-tree data structure

C Matlab Codes

D C++ Codes

Subject index

Author index

Mevcut:*

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Table of Contents