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 information with tremendous complex features deliver new demanding situations to this "old" set of rules. This booklet addresses those demanding situations and makes novel contributions in constructing theoretical frameworks for K-means distances and K-means established consensus clustering, opting for the "dangerous" uniform influence and zero-value quandary of K-means, adapting correct measures for cluster validity, and integrating K-means with SVMs for infrequent category research. This booklet not just enriches the clustering and optimization theories, but in addition presents solid suggestions for the sensible use of K-means, in particular for vital projects similar to community intrusion detection and credits fraud prediction. The thesis on which this booklet relies has received the "2010 nationwide first-class Doctoral Dissertation Award", the top honor for no more than a hundred PhD theses consistent with yr 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