Fuzzy clustering with the entropy of attribute weights.

For many datasets, it is a difficult work to seek a proper cluster structure which covers the entire feature set. To extract the important features and improve the clustering, the maximum-entropy-regularized weighted fuzzy c-means (EWFCM) algorithm is proposed in this paper. A new objective function is developed in the proposed algorithm to achieve the optimal clustering result by minimizing the dispersion within clusters and maximizing the entropy of attribute weights simultaneously. Then the kernelization of proposed algorithm is realized for clustering the data with 'non-spherical' shaped clusters. Experiments on synthetic and real-world datasets have demonstrated the efficiency and superiority of the presented algorithms.