Title: Fuzzy V(variation)-level clustering

Authors: J.M. Lakshmi; G. Raju

Addresses: Manonmaniam Sundaranar University, Tirunelvelli, Tamilnadu, India ' Department of Information Technology, Faculty of Engineering and Technology, Kannur University, Kannur, Kerala, India

Abstract: Clustering is one of the important techniques in data mining as well as one of the most fundamental issues in pattern recognition and business intelligence methods. Traditional conventional methods and evolutionary bio-inspired methods are applied for detecting hard clusters. In most of the real time problems, identification and analysis of hard clusters is impossible or may not be suitable for further analysis and prediction. It leads the requirement for soft clustering identification using fuzzy. The paper proposes about one such fuzzy soft clustering model using V(variation)-level to remove sharp crisp boundaries between clusters. The proposed V-level fuzzy clustering algorithm is capable of handling mixed type data items as well. Experiments were done with both deterministic crisp algorithms, and fuzzy algorithms and the results were compared to observe the efficiency of the model to form soft clusters.

Keywords: data mining; fuzzy C-means clustering; FCM; fuzzy clustering; fuzzy set theory; fuzzy V-level; intelligent paradigms; soft clusters; optimisation; unique variation level.

DOI: 10.1504/IJAIP.2017.081178

International Journal of Advanced Intelligence Paradigms, 2017 Vol.9 No.1, pp.32 - 57

Received: 06 Dec 2014
Accepted: 03 Jun 2015

Published online: 26 Dec 2016 *

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