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 complex features convey new demanding situations to this "old" set of rules. This ebook addresses those demanding situations and makes novel contributions in setting up theoretical frameworks for K-means distances and K-means dependent consensus clustering, selecting the "dangerous" uniform impression and zero-value problem 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 in addition offers reliable counsel for the sensible use of K-means, in particular for very important projects reminiscent of community intrusion detection and credits fraud prediction. The thesis on which this publication is predicated has received the "2010 nationwide first-class 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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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