Hyperbolic Fuzzy C-Means with Adaptive Weight-based Filtering for Efficient Clustering

TL;DR

This paper introduces HypeFCM, a hyperbolic geometry-based fuzzy clustering algorithm with adaptive filtering, designed to effectively handle complex, high-dimensional, and non-Euclidean data structures, outperforming traditional methods.

Contribution

The paper presents a novel hyperbolic fuzzy clustering algorithm that incorporates adaptive weight filtering and hyperbolic metrics, addressing limitations of traditional Euclidean-based fuzzy clustering.

Findings

HypeFCM outperforms conventional fuzzy clustering in non-Euclidean datasets.

The algorithm effectively captures complex hierarchical data structures.

Experimental results demonstrate robustness and improved accuracy.

Abstract

Clustering algorithms play a pivotal role in unsupervised learning by identifying and grouping similar objects based on shared characteristics. Although traditional clustering techniques, such as hard and fuzzy center-based clustering, have been widely used, they struggle with complex, high-dimensional, and non-Euclidean datasets. In particular, the fuzzy $C$-Means (FCM) algorithm, despite its efficiency and popularity, exhibits notable limitations in non-Euclidean spaces. Euclidean spaces assume linear separability and uniform distance scaling, limiting their effectiveness in capturing complex, hierarchical, or non-Euclidean structures in fuzzy clustering. To overcome these challenges, we introduce Filtration-based Hyperbolic Fuzzy C-Means (HypeFCM), a novel clustering algorithm tailored for better representation of data relationships in non-Euclidean spaces. HypeFCM integrates the principles of fuzzy clustering with hyperbolic geometry and employs a weight-based filtering mechanism to improve performance. The algorithm initializes weights using a Dirichlet distribution and iteratively refines cluster centroids and membership assignments based on a hyperbolic metric in the Poincar\'e Disc model. Extensive experimental evaluations on $6$ synthetic and $12$ real-world datasets demonstrate that HypeFCM significantly outperforms conventional fuzzy clustering methods in non-Euclidean settings, underscoring its robustness and effectiveness.