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, centre-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. Suitable as a textbook for an introductory course in cluster analysis or as source material for a graduate-level introduction to data mining.

Guojun Gan is a Ph.D. candidate in the Department of Mathematics and Statistics at York University, Ontario, Canada.

Chaoqun Ma is Professor and the Deputy Dean of the College of Business Administration at Hunan University, People's Republic of China.

Jianhong Wu is a Senior Canada Research Chair in Applied Mathematics at York University, Ontario, Canada.