By Junjie Wu
Nearly we all know K-means set of rules within the fields of information mining and company intelligence. however the ever-emerging info with super advanced features carry new demanding situations to this "old" set of rules. This e-book addresses those demanding situations and makes novel contributions in constructing theoretical frameworks for K-means distances and K-means established consensus clustering, selecting the "dangerous" uniform impression and zero-value issue of K-means, adapting correct measures for cluster validity, and integrating K-means with SVMs for infrequent classification research. This booklet not just enriches the clustering and optimization theories, but additionally offers sturdy suggestions for the sensible use of K-means, particularly for very important initiatives resembling community intrusion detection and credits fraud prediction. The thesis on which this e-book is predicated has gained the "2010 nationwide first-class Doctoral Dissertation Award", the top honor for no more than a hundred PhD theses in step with yr in China.
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