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 facts with tremendous advanced features carry 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 established consensus clustering, opting for the "dangerous" uniform impression and zero-value limitation of K-means, adapting correct measures for cluster validity, and integrating K-means with SVMs for infrequent type research. This booklet not just enriches the clustering and optimization theories, but additionally offers sturdy tips for the sensible use of K-means, specifically for very important projects similar to community intrusion detection and credits fraud prediction. The thesis on which this e-book is predicated has received the "2010 nationwide very good Doctoral Dissertation Award", the top honor for no more than a hundred PhD theses according to 12 months in China.
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Additional info for Advances in K-means Clustering: A Data Mining Thinking (Springer Theses)
Advances in K-means Clustering: A Data Mining Thinking (Springer Theses) by Junjie Wu