An Improved Parallel Method for Computing Rough Set Approximations

Parallel computing refers to the practice of exploiting parallelism in computing to achieve higher performance. Rough set theory plays a fundamental role in data analysis, which was extensively used in the context of data mining. The lower and upper approximations are the basic tools in rough set theory. The fast calculation of approximations can effectively improve the efficiency of rough set theory-based approaches. In this paper, we propose a new parallel strategy for computing approximations, which is able to exploit parallelism at all levels of the computation. An illustrative example is given to demonstrate the effectiveness and validity of the proposed method.