The only thorough, comprehensive book available on clustering

From two of the best-known experts in the field comes the first book to take a truly comprehensive look at clustering. The book begins with a complete introduction to cluster analysis in which readers will become familiarized with classification and clustering; definition of clusters; clustering applications; and the literature of clustering algorithms. The authors then present a detailed outline of the book's content and go on to explore:

• Proximity measures

• Hierarchical clustering

• Partition clustering

• Neural network-based clustering

• Kernel-based clustering

• Sequential data clustering

• Large-scale data clustering

• Data visualization and high-dimensional data clustering

• Cluster validation

The authors assume no previous background in clustering and their generous inclusion of examples and references help make the subject matter comprehensible for readers of varying levels and backgrounds. The book is intended as a professional reference for computer scientists and applied mathematicians working with data-intensive applications, and for computational intelligence researchers who use clustering for feature selection or data reduction. Its selection of homework exercises also makes it appropriate as a textbook for graduate students in mathematics, science, and engineering.

The clustering coefficient of a vertex in a graph quantifies how close the vertex and its neighbors are to being a complete graph, or clique. Here Xu and Wunsch (both electrical and computer engineering, Missouri U. of Science and Technology) begin their classroom text and reference by first explaining cluster analysis, then move on to describe proximity measures, hierarchical clustering, partition clustering, neural network-based clustering, kernel-based clustering, sequential data clustering, large-scale data clustering, data visualization and high-dimensional data clustering, and cluster validation. Xu and Wunsch provide a wealth of examples and carefully phrased instructions, making this useful even for those with no previous background in clustering. They also include reference that guide readers of varying levels and backgrounds through their study. Annotation ©2009 Book News, Inc., Portland, OR (booknews.com)

This is the first book to take a truly comprehensive look at clustering. It begins with an introduction to cluster analysis and goes on to explore: proximity measures; hierarchical clustering; partition clustering; neural network-based clustering; kernel-based clustering; sequential data clustering; large-scale data clustering; data visualization and high-dimensional data clustering; and cluster validation. The authors assume no previous background in clustering and their generous inclusion of examples and references help make the subject matter comprehensible for readers of varying levels and backgrounds.

Written by two of the best-known experts in the field, Clustering is the only thoroughly comprehensive text on the subject currently available. The book looks at the full range of clustering and provides enough detail to allow users to select the method that best fits their application. A generous use of examples and references make the subject matter clear for readers of different levels, including graduate students, computer scientists, and applied mathematicians.

Rui Xu, PhD, is a Research Associate in the Department of Electrical and Computer Engineering at Missouri University of Science and Technology. His research interests include computational intelligence, machine learning, data mining, neural networks, pattern classification, clustering, and bioinformatics. Dr. Xu is a member of the IEEE, the IEEE Computational Intelligence Society (CIS), and Sigma Xi.

Donald C. Wunsch II, PhD, is the M.K. Finley Missouri Distinguished Professor at Missouri University of Science and Technology. His key contributions are in adaptive resonance and reinforcement learning hardware and applications, neurofuzzy regression, improved Traveling Salesman Problem heuristics, clustering, and bioinformatics. He is an IEEE Fellow, the 2005 International Neural Networks Society (INNS) President, and Senior Fellow of the INNS.