In this paper, the authors present a variation of Fuzzy C-Means (FCM) algorithm that provides data clustering. The proposed algorithm incorporates the local spatial information in a novel fuzzy way. The new algorithm is called Weighted Fuzzy Local Information C-Means (WFLICM). WFLICM can overcome the disadvantages of the known fuzzy c-means algorithm and at the same time enhances the clustering performance. The major characteristic of WFLICM is the use of a fuzzy local similarity measure, aiming to guarantee noise insensitiveness and information detail preservation.