Publication 2013 Issue No. 10 - Oct. Abstract - Clustering based on enhanced α-expansion move
Clustering based on enhanced α-expansion move
Oct. 2013 (vol. 25 no. 10)
pp. 2206-2216
 ASCII Text x Yun Zheng, Pei Chen, "Clustering based on enhanced α-expansion move," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 10, pp. 2206-2216, Oct., 2013.
 BibTex x @article{ 10.1109/TKDE.2012.202,author = { Yun Zheng and Pei Chen},title = {Clustering based on enhanced α-expansion move},journal ={IEEE Transactions on Knowledge and Data Engineering},volume = {25},number = {10},issn = {1041-4347},year = {2013},pages = {2206-2216},doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.202},publisher = {IEEE Computer Society},address = {Los Alamitos, CA, USA},}
 RefWorks Procite/RefMan/Endnote x TY - JOURJO - IEEE Transactions on Knowledge and Data EngineeringTI - Clustering based on enhanced α-expansion moveIS - 10SN - 1041-4347SP2206EP2216EPD - 2206-2216A1 - Yun Zheng, A1 - Pei Chen, PY - 2013KW - Clustering algorithmsKW - Approximation algorithmsKW - Belief propagationKW - MinimizationKW - LabelingKW - Random variablesKW - MeasurementKW - $(\alpha)$-expansionKW - Clustering algorithmsKW - Approximation algorithmsKW - Belief propagationKW - MinimizationKW - LabelingKW - Random variablesKW - MeasurementKW - graph cutKW - Exemplar-based clusteringKW - MRFVL - 25JA - IEEE Transactions on Knowledge and Data EngineeringER -
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
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 and Data Engineering, vol. 25, no. 10, pp. 2206-2216, Oct. 2013, doi:10.1109/TKDE.2012.202