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Variance Based Moving K-Means Algorithm

Vijay, Vibin and Raghunath, V P and Singh, Amarjot and Omkar, S N (2017) Variance Based Moving K-Means Algorithm. In: IEEE International Advance Computing Conference, JAN 05-07, 2017, VNR Vignana Jyothi Insti Engn & Technol, Hyderabad, INDIA, pp. 841-847.

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Official URL: http://dx.doi.org/10.1109/IACC.2017.0173


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.

Item Type: Conference Proceedings
Series.: IEEE International Advance Computing Conference
Publisher: IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Additional Information: Copy right for the article belong to IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 04 Apr 2018 18:50
Last Modified: 04 Apr 2018 18:50
URI: http://eprints.iisc.ac.in/id/eprint/59490

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