An attempt of hybridization of generalized dynamic reducts and a heuristic attribute reduction using reduced decision tables

Mining from big data is one of the hottest topics in current trends of computer science. Attribute reduction to compute relative reducts from a given dataset is a key technique to use rough set theory as a tool in data mining, however, attribute reduction from a given dataset with numerous objects and attributes is very difficult. In this paper, to achieve attribute reduction from data with numerous objects and attributes, we try to combine generalized dynamic reducts and a heuristic attributed reduction using reduced decision tables.

I. Introduction

Mining from big data is one of the hottest topics in current trends of computer science. Rough set theory, proposed by Pawlak [1], [2], presents mathematical foundations for approximation of concepts and reasoning about data. Attribute reduction to compute relative reducts from a given dataset is a key technique to use rough set theory as a tool in data mining. However, attribute reduction from big data, i.e., data with numerous objects and attributes, is very difficult because the number of objects and attributes severely affects computational complexity of attribute reduction. Therefore some statistical approach may be practical for attribute reduction from big data.