A Knowledge Mining Model for Ranking Institutions using Rough Computing with Ordering Rules and Formal Concept analysis

TL;DR

This paper proposes a novel knowledge mining model that combines rough set theory, ordering rules, and formal concept analysis to improve decision-making from inconsistent and imprecise data.

Contribution

It introduces a two-stage process using rough set on intuitionistic fuzzy approximation and formal concept analysis for enhanced knowledge extraction.

Findings

Effective handling of inconsistent and imprecise data.

Improved identification of vital factors affecting decisions.

Enhanced knowledge exploration through combined methods.

Abstract

Emergences of computers and information technological revolution made tremendous changes in the real world and provides a different dimension for the intelligent data analysis. Well formed fact, the information at right time and at right place deploy a better knowledge.However, the challenge arises when larger volume of inconsistent data is given for decision making and knowledge extraction. To handle such imprecise data certain mathematical tools of greater importance has developed by researches in recent past namely fuzzy set, intuitionistic fuzzy set, rough Set, formal concept analysis and ordering rules. It is also observed that many information system contains numerical attribute values and therefore they are almost similar instead of exact similar. To handle such type of information system, in this paper we use two processes such as pre process and post process. In pre process we use rough set on intuitionistic fuzzy approximation space with ordering rules for finding the knowledge whereas in post process we use formal concept analysis to explore better knowledge and vital factors affecting decisions.