JACIII Vol.16 No.7 pp. 814-818
doi: 10.20965/jaciii.2012.p0814


Sequential Regression Models with Pairwise Constraints Using Noise Clusters

Hengjin Tang and Sadaaki Miyamoto

Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba-shi, Ibaraki, 305-8573, Japan

November 29, 2011
September 25, 2012
November 20, 2012
switching regression models, constrained clustering, pairwise constraints, sequential clustering
Switching regression models are useful in a variety of real applications. Semi-supervised clustering with pairwise constraints is also well-known to be important and many researchers recently study this subject. In spite of their usefulness, there is one drawback: the results have a strong dependency on the predefined number of clusters. To avoid this drawback, we use a method of sequentially extracting one cluster at a time using noise-detecting method, and propose constrained switching regressionmodels which enables an automatic determination of clusters. We show the effectiveness of the proposed method by using numerical examples.
Cite this article as:
H. Tang and S. Miyamoto, “Sequential Regression Models with Pairwise Constraints Using Noise Clusters,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.7, pp. 814-818, 2012.
Data files:
  1. [1] S. M. Goldfeld and R. E. Quandt, “Techniques for Estimating Switching Regressions,” In: Studies in Nonlinear Estimation, S. M. Goldfeld and R. E. Quandt (Eds.), pp. 3-35, Ballinger, Cambridge, Massachusetts, 1976.
  2. [2] R. E. Quandt, “A New Approach to Estimating Switching Regressions,” J. of the American Statistical Association, Vol.67, pp. 306-310, 1972.
  3. [3] S. Basu, M. Bilenko, and R. J.Mooney, “A Probabilistic Framework for Semi-Supervised Clustering,” Proc. of the Tenth ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD-2004), pp. 59-68, 2004.
  4. [4] O. Chapelle, B. Schölkopf, and A. Zien, “Semi-Supervised Learning,” The MIT Press, Cambridge, Massachusetts, 2006.
  5. [5] K.Wagstaff, C. Cardie, S. Rogers, and S. Schröedl, “Constrained Kmeans Clustering with Background Knowledge,” Proc. of the Eighteenth Int. Conf. on Machine Learning (ICML-2001), pp. 577-584, 2001.
  6. [6] S. Miyamoto, Y. Kuroda, and K. Arai, “Algorithms for Sequential Extraction of Clusters by Possibilistic Method and Comparison with Mountain Clustering,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.12, No.5, 2008.
  7. [7] S. Miyamoto and K. Arai, “Different Sequential Clustering Algorithms and Sequential Regression Models,” Proc. of 2009 IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE2009), pp. 1107-1112, 2009.
  8. [8] R. N. Davé and R. Krishnapuram, “Robust clustering methods: a unified view,” IEEE Trans. on Fuzzy Systems, Vol.5, No.2, pp. 270-293, 1997.
  9. [9] R. N. Davé, “Characterization and detection of noise in clustering,” Pattern Recognition Letters, Vol.12, pp. 657-664, 1991.
  10. [10] K. Wagstaff and C. Cardie, “Clustering with Instance-level Constraints,” Proc. of the Seventeenth Int. Conf. on Machine Learning (ICML-2000), pp. 1103-1110, 2000.
  11. [11] R. N. Davé and S. Sen, “On Generalizing the Noise Clustering Algorithms,” Proc. of the Seventh IFSA World Congress (IFSA ’97), Vol.3, pp. 205-210, 1997.

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

Last updated on May. 10, 2024