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

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.