Rough set model for constraint-based multi-dimensional association rule mining

This paper presents a rough set model for constraint-based multidimensional association rule mining. It first overviews the progress in constraint-based multi-dimensional association rule mining. It then applies the constraints on the rough set model. To set up a decision table, it adopts the user voting and the thresholds on condition granules and decision granules. Finally it employs the extended random sets to generate interesting rules. It shows that this rough set model will effectively improve the quality of association rule mining by reducing the attributes greatly in the vertical direction and clustering the records clearly in the horizontal direction. To describe the association among the attributes, it constructs an ontology and presents a new concept of an association table. The construction of a tuple in an association table indicates the relationship among different levels on the ontology towards decision support.