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http://dx.doi.org/10.5391/IJFIS.2011.11.3.178

Medoid Determination in Deterministic Annealing-based Pairwise Clustering  

Lee, Kyung-Mi (Dept. of Computer Science, Chungbuk National University, and PT-ERC)
Lee, Keon-Myung (Dept. of Computer Science, Chungbuk National University, and PT-ERC)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.11, no.3, 2011 , pp. 178-183 More about this Journal
Abstract
The deterministic annealing-based clustering algorithm is an EM-based algorithm which behaves like simulated annealing method, yet less sensitive to the initialization of parameters. Pairwise clustering is a kind of clustering technique to perform clustering with inter-entity distance information but not enforcing to have detailed attribute information. The pairwise deterministic annealing-based clustering algorithm repeatedly alternates the steps of estimation of mean-fields and the update of membership degrees of data objects to clusters until termination condition holds. Lacking of attribute value information, pairwise clustering algorithms do not explicitly determine the centroids or medoids of clusters in the course of clustering process or at the end of the process. This paper proposes a method to identify the medoids as the centers of formed clusters for the pairwise deterministic annealing-based clustering algorithm. Experimental results show that the proposed method locate meaningful medoids.
Keywords
clustering; data analysis; deterministic annealing; pairwise clustering;
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  • Reference
1 C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
2 B. Clarke, E. Fokoue, H. H. Zhang, Principles and Theory for Data Mining and Machine Learning, Springer, 2009.
3 A. K. Jain, M. N. Murty, P. J. Flynn, "Data Clustering: A Review, ACM Computing Surveys," vol.31, no.3, pp.264-323, 1999.   DOI   ScienceOn
4 P. -N.Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Addison-Wesley, 2006.
5 T. Hofmann, J. M. Buhmann, "Pairwise Data Clustering by Deterministic Annealing," IEEE Trans. on PAMI, vpl.19, no.1, pp.1-14, 1997.   DOI   ScienceOn
6 J. Han, M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2006.
7 K. Rose, "Deterministic Annealing for Clustering, Compression, Classification, Regression, and Related Optimization Problems, Proc. of the IEEE," vol.86, no.11, pp.2210-2239, 1998.   DOI   ScienceOn
8 G. Fung, A Comprehensive Overview of Basic Clustering Algorithms, June 2001.
9 Z. Yanjie, W. Shuanhu, "A Pairwise Clustering based Biclustering Method," Proc. of 2nd Int. Conf. on Signal Processing Systems, pp.V1.311-314, 2010.
10 K. A. Arai, R. Barakbah, "Hierarchical K-means: an algorithm for centroids initiliazation for K-means," Reports of the Faculty of Science and Engineering, Saga University, vol.36, no.1, pp.25-31, 2007.
11 X. Yang, Q. Song, A. Cao, "A Weighted Deterministic Annealing Algorithm for Data Clustering," Int. J. of Computational Intelligence Research, vol.2, no.1, pp.81-85, 2006.