Fuzzy Clustering Data Given on the Ordinal Scale Based on Membership and Likelihood Functions Sharing

Abstract

A task of clustering data given in the ordinal scale under conditions of overlapping clusters has been considered. It's proposed to use an approach based on memberhsip and likelihood functions sharing. A number of performed experiments proved effectiveness of the proposed method. The proposed method is characterized by robustness to outliers due to a way of ordering values while constructing membership functions.

International Journal of Intelligent Systems and Applications(IJISA), Vol. 9, No. 2, February 2017