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Proximity multi-sphere support vector clustering

journal contribution
posted on 2013-06-01, 00:00 authored by T Le, D Tran, Phuoc NguyenPhuoc Nguyen, W Ma, D Sharma
Support vector data description constructs an optimal hypersphere in feature space as a description of a data set. This hypersphere when mapped back to input space becomes a set of contours, and support vector clustering (SVC) employs these contours as cluster boundaries to detect clusters in the data set. However real-world data sets may have some distinctive distributions and hence a single hypersphere cannot be the best description. As a result, the set of contours in input space does not always detect all clusters in the data set. Another issue in SVC is that in some cases, it cannot preserve proximity notation which is crucial for cluster analysis, that is, two data points that are close to each other can be assigned to different clusters using cluster labelling method of SVC. To overcome these drawbacks, we propose Proximity Multi-sphere Support Vector Clustering which employs a set of hyperspheres to provide a better data description for data sets having distinctive distributions and a proximity graph to favour the proximity notation. Experimental results on different data sets are presented to evaluate the proposed clustering technique and compare it with SVC and other clustering techniques.

History

Journal

Neural computing and applications

Volume

22

Pagination

1309-1319

Location

London, Eng.

ISSN

0941-0643

eISSN

1433-3058

Language

eng

Publication classification

C Journal article, C1.1 Refereed article in a scholarly journal

Copyright notice

2012, Springer

Issue

7-8

Publisher

Springer