A Visual Quality Index for Fuzzy C-Means

TL;DR

This paper introduces a new visual quality index for fuzzy C-means clustering that aids in accurately determining the optimal number of clusters, validated through extensive experiments.

Contribution

A novel visual, graph-based quality index for fuzzy clustering that improves the estimation of the optimal number of clusters across datasets.

Findings

The proposed index outperforms existing quality indices in accuracy.

The visual approach provides intuitive insights into cluster validity.

Extensive experiments confirm the effectiveness of the new index.

Abstract

Cluster analysis is widely used in the areas of machine learning and data mining. Fuzzy clustering is a particular method that considers that a data point can belong to more than one cluster. Fuzzy clustering helps obtain flexible clusters, as needed in such applications as text categorization. The performance of a clustering algorithm critically depends on the number of clusters, and estimating the optimal number of clusters is a challenging task. Quality indices help estimate the optimal number of clusters. However, there is no quality index that can obtain an accurate number of clusters for different datasets. Thence, in this paper, we propose a new cluster quality index associated with a visual, graph-based solution that helps choose the optimal number of clusters in fuzzy partitions. Moreover, we validate our theoretical results through extensive comparison experiments against state-of-the-art quality indices on a variety of numerical real-world and artificial datasets.