Generalized Association Rule Mining Algorithms Based on Multidimensional Data

Abstract

This paper proposes a new formalized definition of generalized association rule based on Multidimensional data. The algorithms named BorderLHSs and GenerateLHSs-Rule are designed for generating generalized association rule from multi-level frequent item sets based on Multidimensional Data. Experiment shows that the algorithms proposed in this paper are more efficiency, generate less redundant rules and have good performance in flexibility, scalability and complexity.