single-jc.php

JACIII Vol.16 No.1 pp. 174-179
doi: 10.20965/jaciii.2012.p0174
(2012)

Paper:

Views over last 60 days: 441

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

Received:
January 15, 2011
Accepted:
October 3, 2011
Published:
January 20, 2012
Creative Commons License
Keywords:
semi-supervised clustering, agglomerative hierarchical clustering, centroid method, clusterwise tolerance, pairwise constraints
Abstract

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.

Cite this article as:
Yukihiro Hamasuna, Yasunori Endo, and
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.
Data files:
References
  1. [1] “Semi-Supervised Learning,” O. Chapelle, B. Schoölkopf, and A. Zien (Eds.), MIT Press, 2006.
  2. [2] J. C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms,” Plenum Press, New York, 1981.
  3. [3] S. Miyamoto, H. Ichihashi, and K. Honda, “Algorithms for Fuzzy Clustering,” Springer, Heidelberg, 2008.
  4. [4] 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.
  5. [5] 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.
  6. [6] 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.
  7. [7] 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)
  8. [8] 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.
  9. [9] 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.
  10. [10] 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.
  11. [11] 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.
  12. [12] 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.
  13. [13] 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.
  14. [14] 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.
  15. [15] 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.
  16. [16] 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.
  17. [17] S. Miyamoto, “Fuzzy Sets in Information Retrieval and Cluster Analysis,” Kluwer Dordrecht, 1990.
  18. [18] S. Miyamoto, “Introduction to Cluster Analysis: Theory and Applications of Fuzzy Clustering,” Morikita-Shuppan, Tokyo, 1999. (in Japanse)
  19. [19] L. Hubert and P. Arabie, “Comparing Partitions,” J. of Classification, Vol.2, No.1, pp. 193-218, 1985.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 International License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Sep. 24, 2021