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Average Overlap for Clustering Incomplete Data using Symmetric Non-Negative Matrix Factorization

Chaudhari, Sneha and Murty, Narasimha M (2014) Average Overlap for Clustering Incomplete Data using Symmetric Non-Negative Matrix Factorization. In: 22nd International Conference on Pattern Recognition (ICPR), AUG 24-28, 2014, Swedish Soc Automated Image Anal, Stockholm, SWEDEN, pp. 1431-1436.

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Official URL: http://dx.doi.org/10.1109/ICPR.2014.255

Abstract

Clustering techniques which can handle incomplete data have become increasingly important due to varied applications in marketing research, medical diagnosis and survey data analysis. Existing techniques cope up with missing values either by using data modification/imputation or by partial distance computation, often unreliable depending on the number of features available. In this paper, we propose a novel approach for clustering data with missing values, which performs the task by Symmetric Non-Negative Matrix Factorization (SNMF) of a complete pair-wise similarity matrix, computed from the given incomplete data. To accomplish this, we define a novel similarity measure based on Average Overlap similarity metric which can effectively handle missing values without modification of data. Further, the similarity measure is more reliable than partial distances and inherently possesses the properties required to perform SNMF. The experimental evaluation on real world datasets demonstrates that the proposed approach is efficient, scalable and shows significantly better performance compared to the existing techniques.

Item Type: Conference Poster
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Additional Information: Copy right for this article belongs to the IEEE COMPUTER SOC, 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 24 Sep 2015 06:47
Last Modified: 24 Sep 2015 06:47
URI: http://eprints.iisc.ernet.in/id/eprint/52423

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