Clustering based integration of personal information using Weighted Fuzzy Local Information C-Means Algorithm
S. Santhosh Baboo, P. S. Lal Priya, Devashree Vaishnav · 2013
This paper presents 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. Experiments performed on synthetic and real-world databases like ration card, passport and voter id show that WFLICM algorithm is effective and efficient, providing robustness to noisy data and faster retrieval of information.