By Junjie Wu

Nearly we all know K-means set of rules within the fields of information mining and enterprise intelligence. however the ever-emerging information with super advanced features convey new demanding situations to this "old" set of rules. This publication addresses those demanding situations and makes novel contributions in developing theoretical frameworks for K-means distances and K-means dependent consensus clustering, deciding upon the "dangerous" uniform influence and zero-value predicament of K-means, adapting correct measures for cluster validity, and integrating K-means with SVMs for infrequent category research. This publication not just enriches the clustering and optimization theories, but additionally offers stable tips for the sensible use of K-means, particularly for vital projects resembling community intrusion detection and credits fraud prediction. The thesis on which this booklet relies has received the "2010 nationwide very good Doctoral Dissertation Award", the top honor for no more than a hundred PhD theses in line with 12 months in China.

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