Abstract:

On the condition of losing less information and retaining less data,  the data were refined by the data reduction technique. The proposed approximation algorithm for fuzzy c-means clustering was used to estimate the cluster centers. Combined with validity indexed and estimated centers, FCM can execute unsupervised clustering. The proposed algorithm improved the computational efficiency and performance of the conventional unsupervised fuzzy c-means clustering algorithm. The contrast experimental results with conventional algorithms show that the proposed algorithm has a relatively high precision and efficiency. It can obtain the cluster number more accurately than the conventional algorithm.

Key words: fuzzy c-means, cluster validity, unsupervised clustering, data reduction