E-grāmata: Clustering illustrated edition [Wiley Online]

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.