Application of the cross-entropy method to clustering and vector quantization
نویسندگان
چکیده
We apply the cross-entropy (CE) method to problems in clustering and vector quantization. The CE algorithm involves the following iterative steps: (a) the generation of clusters according to a certain parametric probability distribution, (b) updating the parameters of this distribution according to the Kullback-Leibler cross-entropy. Through various numerical experiments we demonstrate the high accuracy of the CE algorithm and show that it can generate near-optimal clusters for fairly large data sets. We compare the CE method with well-known clustering and vector quantization methods such as K-means, fuzzy K-means and linear vector quantization, and apply each method to benchmark and image analysis data.
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ورودعنوان ژورنال:
- J. Global Optimization
دوره 37 شماره
صفحات -
تاریخ انتشار 2007