摘要: We build a general and easily applicable clustering theory, which we call crossentropy (shortly CEC), joins the advantages of classical kmeans (easy implementation speed) with those EM (ane invariance ability to adapt clusters desired shapes). Moreover, contrary k-means EM, CEC nds optimal number by automatically removing groups have negative information cost.
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