  Relational association rules reveal patterns hide in multiple tables.
Existing rules are usually evaluated through two measures, namely support and
confidence. However, these two measures may not be enough to describe the
strength of a rule. In this paper, we introduce granular association rules with
four measures to reveal connections between granules in two universes, and
propose three algorithms for rule mining. An example of such a rule might be
"40% men like at least 30% kinds of alcohol; 45% customers are men and 6%
products are alcohol." Here 45%, 6%, 40%, and 30% are the source coverage, the
target coverage, the source confidence, and the target confidence,
respectively. With these measures, our rules are semantically richer than
existing ones. Three subtypes of rules are obtained through considering special
requirements on the source/target confidence. Then we define a rule mining
problem, and design a sandwich algorithm with different rule checking
approaches for different subtypes. Experiments on a real world dataset show
that the approaches dedicated to three subtypes are 2-3 orders of magnitudes
faster than the one for the general case. A forward algorithm and a backward
algorithm for one particular subtype can speed up the mining process further.
This work opens a new research trend concerning relational association rule
mining, granular computing and rough sets.
