POCS-based Clustering Algorithm

TL;DR

This paper introduces a new clustering algorithm based on the POCS method that efficiently finds cluster prototypes by projecting onto convex sets, demonstrating competitive performance on synthetic datasets.

Contribution

The paper presents a novel POCS-based clustering algorithm that uses parallel projections to improve clustering accuracy and speed compared to traditional methods.

Findings

Competitive clustering error rates

Faster execution speed

Effective on synthetic datasets

Abstract

A novel clustering technique based on the projection onto convex set (POCS) method, called POCS-based clustering algorithm, is proposed in this paper. The proposed POCS-based clustering algorithm exploits a parallel projection method of POCS to find appropriate cluster prototypes in the feature space. The algorithm considers each data point as a convex set and projects the cluster prototypes parallelly to the member data points. The projections are convexly combined to minimize the objective function for data clustering purpose. The performance of the proposed POCS-based clustering algorithm is verified through experiments on various synthetic datasets. The experimental results show that the proposed POCS-based clustering algorithm is competitive and efficient in terms of clustering error and execution speed when compared with other conventional clustering methods including Fuzzy C-Means (FCM) and K-means clustering algorithms.