MaxMin Linear Initialization for Fuzzy C-Means

TL;DR

This paper introduces MaxMin Linear, a new efficient initialization method for fuzzy c-means clustering, validated through extensive experiments and a novel validity index, improving clustering quality and reliability.

Contribution

The paper proposes a novel linear initialization algorithm for fuzzy c-means clustering, enhancing convergence and clustering quality.

Findings

MaxMin Linear improves clustering initialization efficiency.

The new validity index TSFD effectively evaluates fuzzy clustering.

Extensive experiments demonstrate the method's robustness across datasets.

Abstract

Clustering is an extensive research area in data science. The aim of clustering is to discover groups and to identify interesting patterns in datasets. Crisp (hard) clustering considers that each data point belongs to one and only one cluster. However, it is inadequate as some data points may belong to several clusters, as is the case in text categorization. Thus, we need more flexible clustering. Fuzzy clustering methods, where each data point can belong to several clusters, are an interesting alternative. Yet, seeding iterative fuzzy algorithms to achieve high quality clustering is an issue. In this paper, we propose a new linear and efficient initialization algorithm MaxMin Linear to deal with this problem. Then, we validate our theoretical results through extensive experiments on a variety of numerical real-world and artificial datasets. We also test several validity indices, including a new validity index that we propose, Transformed Standardized Fuzzy Difference (TSFD).