A feature weighted FCM clustering algorithm based on evolutionary strategy

The fuzzy c-means (FCM) algorithm is one of the effective methods for fuzzy cluster analysis, which has been widely used in unsupervised pattern classification. To consider the different contributions of each dimensional feature of the given samples to be classified, this paper presents a novel FCM clustering algorithm based on the weighted feature. With the clustering validity function as a criterion, the proposed algorithm optimizes the weight matrix using an evolutionary strategy and obtains a better result than the traditional one, which enriches the theory of FCM-type algorithms. The test experiment with real data of IRIS demonstrates the effectiveness of the novel algorithm.