# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1702.01200