Mining the weights of similarity measure through learning

An approach is proposed to minimize a fuzzy feature evaluation index function by genetic algorithms. Since not all evaluation indexes perform well, a cross-entropy is introduced to measure the fuzziness of the evaluation function. Experimental results show that with the function of cross-entropy, a suitable evaluation index is chosen, the fuzziness is reduced and the corresponding clustering is optimized.