Top > JACIII > Comparison of Semi-Supervised Hierarchical Clustering Using Cl ...

single-jc.php

JACIII Vol.16 No.7 pp. 819-824
doi: 10.20965/jaciii.2012.p0819
(2012)

Paper:

Views over last 60 days: 653

Comparison of Semi-Supervised Hierarchical Clustering Using Clusterwise Tolerance

Yukihiro Hamasuna* and Yasunori Endo**

*Department of Informatics, School of Science and Engineering, Kinki University, 3-4-1 Kowakae, Higashi-Osaka, Osaka 577-8502, Japan

**Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

Received:
December 1, 2011
Accepted:
September 25, 2012
Published:
November 20, 2012
Creative Commons License
Keywords:
semi-supervised clustering, hierarchical clustering, clusterwise tolerance, pairwise constraints
Abstract
This paper presents a new semi-supervised agglomerative hierarchical clustering algorithm with the ward method using clusterwise tolerance. Semi-supervised clustering has recently been noted and studied in many research fields. Must-link and cannot-link, called pairwise constraints, are frequently used in order to improve clustering properties in semi-supervised clustering. First, clusterwise tolerance based pairwise constraints are introduced in order to handle mustlink and cannot-link constraints. Next, a new semisupervised hierarchical clustering algorithm with the ward method is constructed based on the above discussions. The effectiveness of the proposed algorithms is, moreover, verified through numerical examples.
Cite this article as:
Y. Hamasuna and Y. Endo, “Comparison of Semi-Supervised Hierarchical Clustering Using Clusterwise Tolerance,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.7, pp. 819-824, 2012.
Data files:
References
  1. [1] O. Chapelle, B. Schoölkopf, and A. Zien (Eds.), “Semi-Supervised Learning,” MIT Press, 2006.
  2. [2] S. Miyamoto, H. Ichihashi, and K. Honda, “Algorithms for Fuzzy Clustering,” Springer, Heidelberg, 2008.
  3. [3] 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.
  4. [4] 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.
  5. [5] 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.
  6. [6] 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, CD-ROM, 2009.
  7. [7] 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.
  8. [8] L. Talavera and J. Béjar, “Integrating declarative knowledge in hierarchical clustering tasks,” Proc. of the Third Int. Symposium on Advances in Intelligent Data Analysis (IDA’99), pp. 211-222, 1999.
  9. [9] 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.
  10. [10] 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.
  11. [11] Y. Hamasuna, Y. Endo, and S. Miyamoto, “On Tolerant Fuzzy c-Means,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.13, No.4, pp. 421-427, 2009.
  12. [12] Y. Hamasuna, Y. Endo, and S. Miyamoto, “Fuzzy c-Means Clustering for Data with Clusterwise Tolerance Based on L2- and L1-Regularization,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.15, No.1, pp. 68-75, 2011.
  13. [13] Y. Endo, R. Murata, H. Haruyama, and S. Miyamoto, “Fuzzy c-Means for Data with Tolerance,” Proc. of Int. Symposium on Nonlinear Theory and Its Applications (Nolta’05), pp. 345-348, 2005.
  14. [14] 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. Symposium on Advanced Intelligent Systems (SCIS & ISIS 2010), pp. 397-400, 2010.
  15. [15] Y. Hamasuna and Y. Endo, “On Semi-supervised Fuzzy c-Means Clustering with Clusterwise Tolerance by Opposite Criteria,” Proc. of 2011 IEEE Int. Conf. on Granular Computing (GrC2011), pp. 225-230, 2011.
  16. [16] Y. Hamasuna, Y. Endo, and S. Miyamoto, “Semi-supervised agglomerative hierarchical clustering using clusterwise tolerance based pairwise constraint,” The 7th Int. Conf. on Modeling Decisions for Artificial Intelligence (MDAI2010), Lecture Notes in Artificial Intelligence LNAI, Vol.6408, pp. 152-162, Springer-Verlag Berlin Heidelberg, 2010.
  17. [17] S. Miyamoto, “Introduction to Cluster Analysis: Theory and Applications of Fuzzy Clustering,” Morikita-Shuppan, Tokyo, 1999 (in Japanese).

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

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

Last updated on Apr. 18, 2024