Neighbourhood rough set model for knowledge acquisition using MapReduce

Abstract

Data mining techniques are used to generate information from enormous amount of raw data collected from different sources so that prediction of future events can be made. Rough set theory, which is used to perform data mining for knowledge acquisition has limitations and hence is not efficient in handling heterogeneous real datasets. In this paper, we use a neighbourhood based rough set model and propose a method to determine reduced neighbourhood subsets derived from samples of the universal set. We compare the accuracy and coverage of the computations obtained by using parallel rough set-based methods using the conventional MapReduce technique. The results provide strong evidence of reduced reasoning time in both the cases. Although the subset formation method defines a range of values to which the rules give a better result of the computational analysis, the covering method reduces the number of rules at some cost of the values computed

Recommended citation: ‘Hiremath, Shruthi, Pallavi Chandra, Anne Mary Joy, and B. K. Tripathy. “Neighbourhood rough set model for knowledge acquisition using MapReduce.” International Journal of Communication Networks and Distributed Systems 15, no. 2-3 (2015): 212-234.’