Improving Bregman K-Means
INTERNATIONAL JOURNAL OF DATA MINING MODELLING AND MANAGEMENT(2014)
摘要
We review Bregman divergences and use them in clustering algorithms which we have previously developed to overcome one of the difficulties of the standard k-means algorithm which is its sensitivity to initial conditions which leads to finding sub-optimal local minima. We show empirical results on artificial and real datasets.
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关键词
clustering, K-means, local optima, Bregman divergences
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