Computation of Reducts Using Topology and Measure of Significance of Attributes

TL;DR

This paper introduces a hybrid method combining topology and rough set theory to efficiently compute attribute reducts, reducing complexity and redundancy in large data sets.

Contribution

It proposes a novel approach integrating topological techniques with attribute significance measures for reduct computation in data mining.

Findings

Reduces computational complexity of reduct calculation.

Effectively eliminates redundant attributes.

Enhances data processing efficiency in large datasets.

Abstract

Data generated in the fields of science, technology, business and in many other fields of research are increasing in an exponential rate. The way to extract knowledge from a huge set of data is a challenging task. This paper aims to propose a hybrid and viable method to deal with an information system in data mining, using topological techniques and the significance of the attributes measured using rough set theory, to compute the reduct, This will reduce the randomness in the process of elimination of redundant attributes, which, in turn, will reduce the complexity of the computation of reducts of an information system where a large amount of data have to be processed.