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

A Comparative Study of Discretization Approaches for Granular Association Rule Mining