A Novel Adaptive Possibilistic Clustering Algorithm

TL;DR

This paper introduces an adaptive possibilistic clustering algorithm that automatically adjusts its parameters during execution, effectively identifying the true number of clusters and adapting to data variations, with proven convergence and demonstrated success on synthetic and real datasets.

Contribution

The paper presents a fully adaptive possibilistic c-means clustering algorithm that dynamically adjusts all parameters, improving cluster detection and flexibility over traditional methods.

Findings

Successfully identifies true number of clusters from overestimates.

Demonstrates convergence behavior through theoretical analysis.

Proves effectiveness on synthetic and real datasets.

Abstract

In this paper a novel possibilistic c-means clustering algorithm, called Adaptive Possibilistic c-means, is presented. Its main feature is that {\it all} its parameters, after their initialization, are properly adapted during its execution. Provided that the algorithm starts with a reasonable overestimate of the number of physical clusters formed by the data, it is capable, in principle, to unravel them (a long-standing issue in the clustering literature). This is due to the fully adaptive nature of the proposed algorithm that enables the removal of the clusters that gradually become obsolete. In addition, the adaptation of all its parameters increases the flexibility of the algorithm in following the variations in the formation of the clusters that occur from iteration to iteration. Theoretical results that are indicative of the convergence behavior of the algorithm are also provided. Finally, extensive simulation results on both synthetic and real data highlight the effectiveness of the proposed algorithm.