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
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.
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