A new unsupervised competitive learning rule is introduced, called the kernel-based maximum entropy learning rule (kMER), for equiprobabilistic topographic map formation. The application envisaged is density-based clustering. An empirical study is conducted to compare the clustering performance of kMER with that of a number of other unsupervised competitive learning rules.
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