  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.
