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

Paper:

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

Received:
November 29, 2011
Accepted:
September 25, 2012
Published:
November 20, 2012
Keywords:
switching regression models, constrained clustering, pairwise constraints, sequential clustering
Abstract

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:
Hengjin Tang and Sadaaki 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:
References
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Last updated on Dec. 01, 2021