Variance Based Moving K-Means Algorithm

TL;DR

This paper introduces VMKM, a new clustering algorithm that improves upon traditional methods by reducing intra-cluster variance and avoiding dead centers, regardless of initial cluster placement, through a novel distance metric and data transfer strategy.

Contribution

The paper presents a variance-based modification of Moving K-Means that enhances clustering quality and robustness across diverse datasets with a novel distance metric and element transfer criteria.

Findings

Outperforms existing clustering methods in multiple datasets

Achieves lower intra-cluster variance and avoids dead centers

Demonstrates robustness to initial cluster center placement

Abstract

Clustering is a useful data exploratory method with its wide applicability in multiple fields. However, data clustering greatly relies on initialization of cluster centers that can result in large intra-cluster variance and dead centers, therefore leading to sub-optimal solutions. This paper proposes a novel variance based version of the conventional Moving K-Means (MKM) algorithm called Variance Based Moving K-Means (VMKM) that can partition data into optimal homogeneous clusters, irrespective of cluster initialization. The algorithm utilizes a novel distance metric and a unique data element selection criteria to transfer the selected elements between clusters to achieve low intra-cluster variance and subsequently avoid dead centers. Quantitative and qualitative comparison with various clustering techniques is performed on four datasets selected from image processing, bioinformatics, remote sensing and the stock market respectively. An extensive analysis highlights the superior performance of the proposed method over other techniques.