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 super complex features convey new demanding situations to this "old" set of rules. This e-book addresses those demanding situations and makes novel contributions in setting up theoretical frameworks for K-means distances and K-means established consensus clustering, picking out the "dangerous" uniform impression and zero-value obstacle of K-means, adapting correct measures for cluster validity, and integrating K-means with SVMs for infrequent type research. This ebook not just enriches the clustering and optimization theories, but in addition offers sturdy advice for the sensible use of K-means, in particular for vital initiatives similar to community intrusion detection and credits fraud prediction. The thesis on which this publication relies has received the "2010 nationwide very good Doctoral Dissertation Award", the top honor for no more than a hundred PhD theses according to yr in China.
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Advances in K-means Clustering: A Data Mining Thinking (Springer Theses) by Junjie Wu