By Junjie Wu
Nearly we all know K-means set of rules within the fields of information mining and company intelligence. however the ever-emerging info with tremendous advanced 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, settling on the "dangerous" uniform impact and zero-value predicament of K-means, adapting correct measures for cluster validity, and integrating K-means with SVMs for infrequent classification research. This booklet not just enriches the clustering and optimization theories, but additionally offers stable tips for the sensible use of K-means, particularly for very important projects resembling community intrusion detection and credits fraud prediction. The thesis on which this e-book relies has received the "2010 nationwide first-class Doctoral Dissertation Award", the top honor for no more than a hundred PhD theses in step with 12 months in China.
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