Data Clustering

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<P>Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, <STRONG>Data Clustering: Algorithms and Applications</STRONG> provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.<BR><BR>The book focuses on three primary aspects of data clustering:</P><UL><LI><EM>Methods</EM>, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization</LI><LI><EM>Domains</EM>, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biologic