By Junjie Wu

Nearly we all know K-means set of rules within the fields of information mining and enterprise intelligence. however the ever-emerging facts with tremendous advanced features convey new demanding situations to this "old" set of rules. This booklet addresses those demanding situations and makes novel contributions in developing theoretical frameworks for K-means distances and K-means established consensus clustering, making a choice on the "dangerous" uniform impression and zero-value hassle of K-means, adapting correct measures for cluster validity, and integrating K-means with SVMs for infrequent category research. This publication not just enriches the clustering and optimization theories, but in addition presents solid advice for the sensible use of K-means, in particular for vital initiatives equivalent to community intrusion detection and credits fraud prediction. The thesis on which this publication relies has gained the "2010 nationwide first-class Doctoral Dissertation Award", the top honor for no more than a hundred PhD theses in keeping with yr in China.

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Facts uncertainty largely exists in lots of purposes, and an doubtful info circulate is a sequence of doubtful tuples that arrive speedily. despite the fact that, conventional thoughts for deterministic info streams can't be utilized to accommodate information uncertainty without delay as a result exponential development of attainable answer house.

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