  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.
