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FUZZY BASED PRIVACY PRESERVED K-MEANS CLUSTERING

Abstract The aim of this paper is to identify the impact of the fuzzy based privacy preserving method in clustering which is one of the important data mining process. Fuzzy member ship functions like Bell shape, S- Shape and PI shape membership functions are applied on standard database to generate sanitised database. Further, various clustering algorithms are applied on the sanitised database and the results are compared. WEKA tool is used for testing K-Means clustering algorithm on privacy preserved database generated using various fuzzy member ship function. This analysis will help to develop new Fuzzy Based privacy preserving clustering techniques and also lead future researches in Privacy preserved data mining. Authors D Murugan, S Selva Rathna Manonmaniam Sundaranar University, India Keywords Clustering, Fuzzy Membership function, Privacy Preserving data mining, WEKA tool

Published By : ICTACT Published In : ICTACT Journal on Soft Computing ( Volume: 10 , Issue: 1 ) Date of Publication : October 2019 DOI :