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 advanced features deliver new demanding situations to this "old" set of rules. This e-book addresses those demanding situations and makes novel contributions in developing theoretical frameworks for K-means distances and K-means dependent consensus clustering, picking out the "dangerous" uniform influence and zero-value problem of K-means, adapting correct measures for cluster validity, and integrating K-means with SVMs for infrequent category research. This e-book not just enriches the clustering and optimization theories, but in addition presents strong suggestions for the sensible use of K-means, particularly for very important projects similar to community intrusion detection and credits fraud prediction. The thesis on which this ebook is predicated has gained the "2010 nationwide first-class Doctoral Dissertation Award", the top honor for no more than a hundred PhD theses according to yr in China.
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Additional resources 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