Variance Based Moving K-Means Algorithm

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.

Modified Graph-theoretic Clustering Algorithm for Mining International Linkages of Philippine Higher Education Institutions

“…Meanwhile, methods and techniques in data mining allow analysis of very large datasets (i.e. big data) to extract and discover previously unknown structures and relations out of huge amount of details [7] for the purpose of knowledge extraction. As such, clustering in the data mining arena aims to establish high intra-cluster and low inter-cluster similarity in data.…”