By Junjie Wu
Nearly we all know K-means set of rules within the fields of knowledge 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 developing theoretical frameworks for K-means distances and K-means dependent consensus clustering, making a choice on the "dangerous" uniform impression and zero-value quandary of K-means, adapting correct measures for cluster validity, and integrating K-means with SVMs for infrequent type research. This publication not just enriches the clustering and optimization theories, but in addition offers strong information for the sensible use of K-means, specially for vital projects comparable to community intrusion detection and credits fraud prediction. The thesis on which this e-book 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 yr in China.
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