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2019 | OriginalPaper | Chapter

Enhanced Possibilistic C-Means Clustering on Big Data While Ensuring Security

Authors : Shriya R. Paladhi, R. Mohan Kumar, A. G. Deepshika Reddy, C. Y. Vinayak, T. P. Pusphavathi

Published in: International Conference on Computer Networks and Communication Technologies

Publisher: Springer Singapore

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Abstract

Data clustering is the most important technique in knowledge discovery and data engineering. Recently, the possibilistic C-means algorithm (PCM) was proposed to address the drawbacks associated with the constrained memberships used in algorithms such as the fuzzy C-means (FCM). Among different variations of clustering, possibilistic C-means (PCM) uses constrained membership functions. But the drawback of PCM converges to coincident clusters. The purpose of this paper is to overcome the drawback of PCM while preserving the privacy of sensitive data. The proposed algorithm in this paper is tensor PCM (TPCM), which makes use of tensor relational data model. TPCM algorithm is further modified as privacy-preserving TPCM algorithm to preserve the privacy by using Brakerski–Gentry–Vaikuntanathan (BGV) technique.

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Title: Enhanced Possibilistic C-Means Clustering on Big Data While Ensuring Security
Authors: Shriya R. Paladhi
R. Mohan Kumar
A. G. Deepshika Reddy
C. Y. Vinayak
T. P. Pusphavathi
Publisher: Springer Singapore
Book: International Conference on Computer Networks and Communication Technologies
Print ISBN: 978-981-10-8680-9

Electronic ISBN: 978-981-10-8681-6

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-981-10-8681-6_53

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