On Agglomerative Hierarchical Clustering Using Clusterwise Tolerance Based Pairwise Constraints
Yukihiro Hamasuna*, Yasunori Endo**,
and Sadaaki Miyamoto**
*Department of Informatics, School of Science and Engineering, Kinki University, 3-4-1 Kowakae, Higashi-Osaka, Osaka 577-8502, Japan
**Department of Risk Engineering, Faculty of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
This paper presents semi-supervised agglomerative hierarchical clustering algorithm using clusterwise tolerance based pairwise constraints. In semi-supervised clustering, pairwise constraints, that is, must-link and cannot-link, are frequently used in order to improve clustering properties. From that sense, we will propose another way named clusterwise tolerance based pairwise constraints to handle must-link and cannot-link constraints in L2-space. In addition, we will propose semi-supervised agglomerative hierarchical clustering algorithm based on it. We will, moreover, show the effectiveness of the proposed method through numerical examples.
and Sadaaki Miyamoto, “On Agglomerative Hierarchical Clustering Using Clusterwise Tolerance Based Pairwise Constraints,” J. Adv. Comput. Intell. Intell. Inform., Vol.16, No.1, pp. 174-179, 2012.
-  “Semi-Supervised Learning,” O. Chapelle, B. Schoölkopf, and A. Zien (Eds.), MIT Press, 2006.
-  J. C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms,” Plenum Press, New York, 1981.
-  S. Miyamoto, H. Ichihashi, and K. Honda, “Algorithms for Fuzzy Clustering,” Springer, Heidelberg, 2008.
-  K. Wagstaff, C. Cardie, S. Rogers, and S. Schroedl, “Constrained k-Means Clustering with Background Knowledge,” Proc. of the 18th Int. Conf. on Machine Learning (ICML 2001), pp. 577-584, 2001.
-  S. Basu, A. Banerjee, and R. J. Mooney, “Active Semi-Supervision for Pairwise Constrained Clustering,” Proc. of the SIAM Int. Conf. on Data Mining (SDM 2004), pp. 333-344, 2004.
-  S. Basu, M. Bilenko, and R. J. Mooney, “A Probabilistic Framework for Semi-Supervised Clustering,” Proc. of the 10th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2004), pp. 59-68, 2004.
-  S. Miyamoto, M. Yamazaki, and A. Terami, “On Semi-Supervised Clustering with Pairwise Constraints,” Proc. of The 7th Int. Conf. on Modeling Decisions for Artificial Intelligence (MDAI 2009), pp. 245-254, 2009. (CD-ROM)
-  B. Yan and C. Domeniconi, “An Adaptive Kernel Method for Semi-Supervised Clustering,” Proc. of 17th European Conf. on Machine Learning (ECML 2006), pp. 521-532, 2006.
-  B. Kulis, S. Basu, I. Dhillon, and R. Mooney, “Semi-Supervised Graph Clustering: a Kernel Approach,” Machine Learning, Vol.74, No.1, pp. 1-22, 2009.
-  L. Talavera and J. Béjar, “Integrating Declarative Knowledge in Hierarchical Clustering Tasks,” Proc. of the Third Int. Symp. on Advances in Intelligent Data Analysis (IDA’99), pp. 211-222, 1999.
-  D. Klein, S. Kamvar, and C. Manning, “From Instance-Level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering,” Proc. of the 19th Int. Conf. on Machine Learning (ICML 2002), pp. 307-314, 2002.
-  I. Davidson and S. S. Ravi, “Agglomerative Hierarchical Clustering with Constraints: Theoretical and Empirical Results,” Proc. of 9th European Conf. on Principles and Practice of Knowledge Discovery in Databases (KDD 2005), pp. 59-70, 2005.
-  Y. Hamasuna, Y. Endo, and S. Miyamoto, “On Tolerant Fuzzy c-Means,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.13, No.4, pp. 421-427, 2009.
-  Y. Endo, R. Murata, H. Haruyama, and S. Miyamoto, “Fuzzy c-Means for Data with Tolerance,” Proc. of Int. Symp. on Nonlinear Theory and Its Applications (Nolta’05), pp. 345-348, 2005.
-  Y. Hamasuna, Y. Endo, and S. Miyamoto, “Semi-Supervised Fuzzy c-Means Clustering Using Clusterwise Tolerance Based Pairwise Constraints,” Proc. of 2010 IEEE Int. Conf. on Granular Computing (GrC2010), pp. 188-193, 2010.
-  Y. Hamasuna and Y. Endo, “Semi-Supervised Fuzzy c-Means Clustering for Data with Clusterwise Tolerance with Pairwise Constraints,” Joint 5th Int. Conf. on Soft Computing and Intelligent Systems and 11th Int. Symp. on Advanced Intelligent Systems (SCIS & ISIS 2010), pp. 397-400, 2010.
-  S. Miyamoto, “Fuzzy Sets in Information Retrieval and Cluster Analysis,” Kluwer Dordrecht, 1990.
-  S. Miyamoto, “Introduction to Cluster Analysis: Theory and Applications of Fuzzy Clustering,” Morikita-Shuppan, Tokyo, 1999. (in Japanse)
-  L. Hubert and P. Arabie, “Comparing Partitions,” J. of Classification, Vol.2, No.1, pp. 193-218, 1985.