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 info with tremendous complex features carry new demanding situations to this "old" set of rules. This publication addresses those demanding situations and makes novel contributions in setting up theoretical frameworks for K-means distances and K-means dependent consensus clustering, deciding on the "dangerous" uniform impact and zero-value problem of K-means, adapting correct measures for cluster validity, and integrating K-means with SVMs for infrequent type research. This e-book not just enriches the clustering and optimization theories, but additionally presents reliable information for the sensible use of K-means, in particular for vital projects equivalent 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 in keeping with 12 months in China.
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