JACIII Vol.19 No.1 pp. 36-42
doi: 10.20965/jaciii.2015.p0036


On Objective-Based Rough c-Regression

Akira Sugawara*, Yasunori Endo**, and Naohiko Kinoshita*

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

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

April 20, 2014
August 25, 2014
January 20, 2015
clustering, rough set, optimization, c-regression
The pattern recognition method of clustering is a technique automatically classifying data into clusters. Among clustering methods, c-regression based on fuzzy set theory, called Fuzzy c-Regression (FCR), is proposed to get a linear dataset structure. The most recent clustering is based on rough set theory called rough clustering, which is less descriptive than fuzzy clustering. A typical rough clustering algorithm is Rough k-Regression (RKR). However, RKR has problems because it depends on initial values and has no optimum index, so we do not know whether a clustering result will be optimal. This paper proposes Rough c-Regression (RCR) based on the optimization of an objective function and demonstrates its effectiveness through numerical examples.
Cite this article as:
A. Sugawara, Y. Endo, and N. Kinoshita, “On Objective-Based Rough c-Regression,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.1, pp. 36-42, 2015.
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Last updated on Jul. 19, 2024