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 super complex features convey 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 dependent consensus clustering, deciding on the "dangerous" uniform influence and zero-value trouble of K-means, adapting correct measures for cluster validity, and integrating K-means with SVMs for infrequent type research. This ebook not just enriches the clustering and optimization theories, but additionally presents solid information for the sensible use of K-means, specifically for very important initiatives similar to community intrusion detection and credits fraud prediction. The thesis on which this booklet is predicated has gained the "2010 nationwide first-class Doctoral Dissertation Award", the top honor for no more than a hundred PhD theses in line 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