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Big Data and Information Analytics (BDIA)
 

A soft subspace clustering algorithm with log-transformed distances

Pages: 93 - 109, Volume 1, Issue 1, January 2016      doi:10.3934/bdia.2016.1.93

 
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Guojun Gan - Department of Mathematics, University of Connecticut, 196 Auditorium Rd, Storrs, CT 06269-3009, United States (email)
Kun Chen - Department of Statistics, University of Connecticut, 215 Glenbrook Road, Storrs, CT 06269, United States (email)

Abstract: Entropy weighting used in some soft subspace clustering algorithms is sensitive to the scaling parameter. In this paper, we propose a novel soft subspace clustering algorithm by using log-transformed distances in the objective function. The proposed algorithm allows users to choose a value of the scaling parameter easily because the entropy weighting in the proposed algorithm is less sensitive to the scaling parameter. In addition, the proposed algorithm is less sensitive to noises because a point far away from its cluster center is given a small weight in the cluster center calculation. Experiments on both synthetic datasets and real datasets are used to demonstrate the performance of the proposed algorithm.

Keywords:  Data clustering, subspace clustering, attribute weighting, $k$-means.
Mathematics Subject Classification:  Primary: 62H30, 68T10, 91C20; Secondary: 62P10.

Received: May 2015;      Revised: August 2015;      Available Online: September 2015.

 References