Semantic partition based association rule mining across multiple databases using abstraction

Abstract

Association rule mining activity is both computationally and I/O intensive. A majority of ARM algorithms reported in the literature is efficient in handling high dimensional data but is single database based. Many enterprises maintain several databases independently to serve different purposes. There could be an implicit association among various parts of such data. In this paper, we investigate a mechanism to generate Association Rules (ARs) between the sets of values which are subsets of domains of attributes occurring in relations present in different databases. In our approach, the relevant databases, relations and attributes are identified using knowledge, multiple navigation paths are generated using data dictionary, a structure is constructed which semantically partitions the resultant relation using this navigation paths. We propose an efficient algorithm which uses this structure to generate ARs. � 2007 IEEE.