Issue No. 10 - Oct. (2013 vol. 25)

ISSN: 1041-4347

pp: 2206-2216

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.202

Yun Zheng , Sch. of Inf. Sci. & Technol., Sun Yat-Sen Univ., Guangzhou, China

Pei Chen , Sch. of Inf. Sci. & Technol., Sun Yat-Sen Univ., Guangzhou, China

ABSTRACT

The exemplar-based data clustering problem can be formulated as minimizing an energy function defined on a Markov random field (MRF). However, most algorithms for optimizing the MRF energy function cannot be directly applied to the task of clustering, as the problem has a high-order energy function. In this paper, we first show that the high-order energy function for the clustering problem can be simplified as a pairwise energy function with the metric property, and consequently it can be optimized by the α-expansion move algorithm based on graph cut. Then, the original expansion move algorithm is improved in the following two aspects: 1) Instead of solving a minimal s-t graph cut problem, we show that there is an explicit and interpretable solution for minimizing the energy function in the clustering problem. Based on this interpretation, a fast α-expansion move algorithm is proposed, which is much more efficient than the graph-cut-based algorithm. 2) The fast α-expansion move algorithm is further improved by extending its move space so that a larger energy value reduction can be achieved in each iteration. Experiments on benchmark data sets show that the enhanced expansion move algorithm has a better performance, compared to other state-of-the-art exemplar-based clustering algorithms.

INDEX TERMS

Clustering algorithms, Approximation algorithms, Belief propagation, Minimization, Labeling, Random variables, Measurement,$(\alpha)$-expansion, Clustering algorithms, Approximation algorithms, Belief propagation, Minimization, Labeling, Random variables, Measurement, graph cut, Exemplar-based clustering, MRF

CITATION

Yun Zheng, Pei Chen, "Clustering based on enhanced α-expansion move",

*IEEE Transactions on Knowledge & Data Engineering*, vol. 25, no. , pp. 2206-2216, Oct. 2013, doi:10.1109/TKDE.2012.202