Parallel Computation of Probabilistic Rough Set Approximations

Abstract

Probabilistic rough sets (PRS), a generalization of Pawlak rough sets, have become increasingly successful in dealing with inconsistent information systems. Over the last few decades, rough sets with a probabilistic approach have been applied extensively for data pre-processing, analysis, and decision rule generation in the areas such as data mining and knowledge discovery, pattern recognition, and machine learning. Finding the approximations, both lower and upper are the fundamental steps in PRS or in any generalization derived from rough set theory. With the massive and rapid increase in data generation, computing approximations effectively using the existing traditional probabilistic approaches is turning out to be a challenging task. Recent advances in parallel processing techniques and tools like MapReduce, Apache Hadoop, and Apache Spark have ushered in the development of computationally efficient methods for the analysis of massively large datasets. This paper presents an algorithm by name parallel algorithm for computing probabilistic rough set approximations (PACPRSA), for computing regions and approximations using PRS in parallel. The results of extensive experimentation suggest that the proposed parallel algorithm evidently performs well in standard scalability metrics and therefore is well suited for application on contemporary large datasets.