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.
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