Advances in K-means Clustering

Gives an overall picture on how to adapt K-means to the clustering of newly emerging big data Establishes a theoretical framework for K-means clustering and cluster validity Studies the dangerous uniform effect and zero-value dilemma of K-means Demonstrates the novel use of K-means for rare class analysis and consensus clustering Based on the thesis that won the 2010 National Excellent Doctoral Dissertation Award of China Includes supplementary material: sn.pub/extras

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