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 advanced features convey 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, deciding upon 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 solid counsel for the sensible use of K-means, in particular for vital projects corresponding to community intrusion detection and credits fraud prediction. The thesis on which this e-book relies 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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Extra 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