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Data Clustering: Theory, Algorithms, and Applications

Gan, Guojun, Ma, Chaoqun, Wu, Jianhong·2007·Society for Industrial and Applied Mathematics eBooks

Abstract

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® programming languages.

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Metadata

Authors
Gan, G., Ma, C., & Wu, J.
Venue
Society for Industrial and Applied Mathematics eBooks
Published
2007-01
OpenAlex
W1594924988
Cited by
1,562
References
0
FWCI
8.45
Type
book
Language
en
Open access
closed

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CoChat. (2026). Citation check: Data Clustering: Theory, Algorithms, and Applications. https://app.cochat.ai/papers/10.1137/1.9780898718348
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Gan, G., Ma, C., & Wu, J. (2007). Data Clustering: Theory, Algorithms, and Applications. Society for Industrial and Applied Mathematics eBooks. https://doi.org/10.1137/1.9780898718348

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