An enhanced ontology based Weighted Fuzzy Local Information C-Means Algorithm for clustering
نویسندگان
چکیده
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 Cmeans 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 preservation. The algorithm works well for ontology database in an XML file format. 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. This paper provides a comparison of the normal database with the ontology database.
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