Abstract
This paper presents a new semi-supervised agglomerative hierarchical clustering algorithm with ward method using clusterwise tolerance. Recently, semi-supervised clustering has been remarked and studied in many research fields. In semi-supervised clustering, must-link and cannot-link called pairwise constraints are frequently used in order to improve clustering properties. First, a clusterwise tolerance based pairwise constraints is introduced in order to handle must-link and cannot-link constraints. Next, a new semi-supervised agglomerative hierarchical clustering algorithm with ward method is constructed based on above discussions. Moreover, the effectiveness of proposed algorithms is verified through numerical examples.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chapelle, O., Schoölkopf, B., Zien, A. (eds.): Semi-Supervised Learning. MIT Press, Cambridge (2006)
Miyamoto, S., Ichihashi, H., Honda, K.: Algorithms for Fuzzy Clustering. Springer, Heidelberg (2008)
Wagstaff, K., Cardie, C., Rogers, S., Schroedl, S.: Constrained k-means clustering with background knowledge. In: Proc. of the 18th International Conference on Machine Learning (ICML 2001), pp. 577–584 (2001)
Basu, S., Banerjee, A., Mooney, R.J.: Active semi-supervision for pairwise constrained clustering. In: Proc. of the SIAM International Conference on Data Mining (SDM 2004), pp. 333–344 (2004)
Basu, S., Bilenko, M., Mooney, R.J.: A probabilistic framework for semi-supervised clustering. In: Proc. of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004), pp. 59–68 (2004)
Miyamoto, S., Yamazaki, M., Terami, A.: On semi-supervised clustering with pairwise constraints. In: Proc. of The 7th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2009), (CD-ROM), pp. 245–254 (2009)
Kulis, B., Basu, S., Dhillon, I., Mooney, R.: Semi-supervised graph clustering: a kernel approach. Machine Learning 74(1), 1–22 (2009)
Talavera, L., Béjar, J.: Integrating declarative knowledge in hierarchical clustering tasks. In: Hand, D.J., Kok, J.N., Berthold, M.R. (eds.) IDA 1999. LNCS, vol. 1642, pp. 211–222. Springer, Heidelberg (1999)
Klein, D., Kamvar, S., Manning, C.: From instance-level constraints to space-level constraints: making the most of prior knowledge in data clustering. In: Proc. of the 19th International Conference on Machine Learning (ICML 2002), pp. 307–314 (2002)
Davidson, I., Ravi, S.S.: Agglomerative hierarchical clustering with constraints: theoretical and empirical results. In: Proc. of 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (KDD 2005), pp. 59–70 (2005)
Hamasuna, Y., Endo, Y., Miyamoto, S.: On Tolerant Fuzzy c-Means. Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII) 13(4), 421–427 (2009)
Hamasuna, Y., Endo, Y., Miyamoto, S.: Fuzzy c-Means Clustering for Data with Clusterwise Tolerance Based on L 2- and L 1-Regularization. Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII) 15(1), 68–75 (2011)
Endo, Y., Murata, R., Haruyama, H., Miyamoto, S.: Fuzzy c-Means for Data with Tolerance. In: Proc. of International Symposium on Nonlinear Theory and Its Applications (Nolta 2005), pp. 345–348 (2005)
Hamasuna, Y., Endo, Y., Miyamoto, S.: Semi-supervised fuzzy c-means clustering using clusterwise tolerance based pairwise constraints. In: Proc. of 2010 IEEE International Conference on Granular Computing (GrC 2010), pp. 188–193 (2010)
Hamasuna, Y., Endo, Y.: Semi-supervised fuzzy c-means clustering for data with clusterwise tolerance with pairwise constraints. In: Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems (SCIS & ISIS 2010), pp. 397–400 (2010)
Hamasuna, Y., Endo, Y., Miyamoto, S.: Semi-supervised agglomerative hierarchical clustering using clusterwise tolerance based pairwise constraints. In: Torra, V., Narukawa, Y., Daumas, M. (eds.) MDAI 2010. LNCS (LNAI), vol. 6408, pp. 152–162. Springer, Heidelberg (2010)
Miyamoto, S.: Introduction to Cluster Analysis: Theory and Applications of Fuzzy Clustering. Morikita-Shuppan, Tokyo (1999) (in Japanese)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hamasuna, Y., Endo, Y., Miyamoto, S. (2011). Semi-supervised Agglomerative Hierarchical Clustering with Ward Method Using Clusterwise Tolerance. In: Torra, V., Narakawa, Y., Yin, J., Long, J. (eds) Modeling Decision for Artificial Intelligence. MDAI 2011. Lecture Notes in Computer Science(), vol 6820. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22589-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-22589-5_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22588-8
Online ISBN: 978-3-642-22589-5
eBook Packages: Computer ScienceComputer Science (R0)