  Granular association rule is a new approach to reveal patterns hide in
many-to-many relationships of relational databases. Different types of data
such as nominal, numeric and multi-valued ones should be dealt with in the
process of rule mining. In this paper, we study multi-valued data and develop
techniques to filter out strong however uninteresting rules. An example of such
rule might be "male students rate movies released in 1990s that are NOT
thriller." This kind of rules, called negative granular association rules,
often overwhelms positive ones which are more useful. To address this issue, we
filter out negative granules such as "NOT thriller" in the process of granule
generation. In this way, only positive granular association rules are generated
and strong ones are mined. Experimental results on the movielens data set
indicate that most rules are negative, and our technique is effective to filter
them out.
