Attribute Reduction Method of Covering Rough Set Based on Dependence Degree

Abstract

Attribute reduction is a hot topic in the field of data mining. Compared with the traditional methods, the attribute reduction algorithm based on covering rough set is more suitable for dealing with numerical data. However, this kind of algorithm is still not efficient enough to deal with large-scale data. In this paper, we firstly propose -Boolean identification matrix of covering rough sets, give the calculation methods of dependence degree and local dependence degree, and further discuss their properties. Secondly, we give two attribute reduction algorithms based on dependence degree and local dependence degree, respectively. Finally, we test the performance of the algorithm through several UCI data sets. Experimental results show that the efficiency of our algorithm has been greatly improved. So it is more suitable for handling large-scale data processing problems, and can have wide application value.