A Distributed Rough Set Theory Algorithm based on Locality Sensitive Hashing for an Efficient Big Data Pre-processing

A big challenge in the knowledge discovery process is to perform big data pre-processing; specifically feature selection. To handle this challenge, Rough Set Theory (RST) has been considered as one of the most powerful techniques as it has much to offer for feature selection. To extend its applicability to big data, a distributed version of RST was developed. However, one of its key challenges is the partitioning of the feature search space in the distributed environment while guaranteeing data dependency. In this paper, we propose a new distributed version of RST based on Locality Sensitive Hashing (LSH), named LSH-dRST, for big data pre-processing. LSH-dRST uses LSH to match similar features into the same bucket and maps the generated buckets into partitions to enable the splitting of the universe in a more appropriate way. We compare LSH-dRST to the standard distributed RST technique which is based on a random partitioning of the universe and demonstrate that our LSH-dRST is not only scalable but also more reliable for feature selection; making it more relevant to big data pre-processing. We also demonstrate that our LSH-dRST ensures the partitioning of the high dimensional feature search space in a more reliable way. Hence, guarantees data dependency in the distributed environment, and ensures a lower computational cost.