A Comparative Study of Discretization Approaches for Granular Association Rule Mining

Abstract

Granular association rule mining is a new relational data mining approach to reveal patterns hidden in multiple tables. The current research of granular association rule mining considers only nominal data. In this paper, we study the impact of discretization approaches on mining semantically richer and stronger rules from numeric data. Specifically, the Equal Width approach and the Equal Frequency approach are adopted and compared. The setting of interval numbers is a key issue in discretization approaches, so we compare different settings through experiments on a well-known real life data set. Experimental results show that: 1) discretization is an effective preprocessing technique in mining stronger rules; 2) the Equal Frequency approach helps generating more rules than the Equal Width approach; 3) with certain settings of interval numbers, we can obtain much more rules than others.