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

TL;DR

This paper introduces a robust fuzzy clustering method for ordinal scale data with overlapping clusters, utilizing shared membership and likelihood functions, and demonstrates its effectiveness through experiments.

Contribution

It proposes a novel fuzzy clustering approach based on shared membership and likelihood functions specifically for ordinal scale data with overlapping clusters.

Findings

Method shows robustness to outliers.

Experiments confirm effectiveness of the clustering approach.

Approach improves clustering accuracy on ordinal data.

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.