By Junjie Wu
Nearly we all know K-means set of rules within the fields of knowledge mining and enterprise intelligence. however the ever-emerging facts with super complex features deliver new demanding situations to this "old" set of rules. This ebook addresses those demanding situations and makes novel contributions in constructing theoretical frameworks for K-means distances and K-means dependent consensus clustering, deciding on the "dangerous" uniform influence and zero-value trouble of K-means, adapting correct measures for cluster validity, and integrating K-means with SVMs for infrequent category research. This ebook not just enriches the clustering and optimization theories, but in addition offers reliable information for the sensible use of K-means, specially for very important projects akin to community intrusion detection and credits fraud prediction. The thesis on which this publication is predicated has gained the "2010 nationwide very good Doctoral Dissertation Award", the top honor for no more than a hundred PhD theses consistent with yr in China.
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Advances in K-means Clustering: A Data Mining Thinking (Springer Theses) by Junjie Wu