JACIII Vol.17 No.3 pp. 371-376
doi: 10.20965/jaciii.2013.p0371


A Parallel Computation Method for Heuristic Attribute Reduction Using Reduced Decision Tables

Yasuo Kudo* and Tetsuya Murai**

*College of Information and Systems, Muroran Institute of Technology, 27-1 Mizumoto, Muroran 050-8585, Japan

**Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo 060-0814, Japan

December 8, 2012
January 20, 2013
May 20, 2013
rough set, attribute reduction, parallel computation, open multiprocessing

In this paper, we propose a parallel computation framework for a heuristic attribute reduction method. Attribute reduction is a key technique to use rough set theory as a tool in data mining. The authors have previously proposed a heuristic attribute reduction method to compute as many relative reducts as possible from a given dataset with numerous attributes. We parallelize our method by using open multiprocessing. We also evaluate the performance of a parallelized attribute reduction method by experiments.

Cite this article as:
Yasuo Kudo and Tetsuya Murai, “A Parallel Computation Method for Heuristic Attribute Reduction Using Reduced Decision Tables,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.3, pp. 371-376, 2013.
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