Indistinguishable Element-Pair Attribute Reduction and Its Incremental Approach

Abstract

Attribute reduction is a popular approach of preprocessing data. Discernibility matrix is a typical method that focuses on attribute reduction. Faced with the processing of modern information systems with large amounts of data and rapid changes, the traditional static discernibility matrix reduction model is powerless. To overcome this shortcoming, this paper first proposes an indistinguishable element pair method that does not need to store discernibility information, which retains the advantages of institution and easy-to-understand, and at the same time effectively solves the problem of space consumption. In order to make the model adapt to the processing of dynamic data sets, we further study the incremental mechanism and design a set of dynamic reduction models, which can adjust the reduction set in time according to the changes of objects. Theoretical analysis and experimental results indicate that the proposed algorithm is obviously superior to the discernibility matrix model, and can effectively deal with the reduction of dynamic data sets.