Mining associations by pattern structure in large relational tables

Association rule mining aims at discovering patterns whose support is beyond a given threshold. Mining patterns composed of items described by an arbitrary subset of attributes in a large relational table represents a new challenge and has various practical applications, including the event management systems that motivated this work. The attribute combinations that define the items in a pattern provide the structural information of the pattern. Current association algorithms do not make full use of the structural information of the patterns: the information is either lost after it is encoded with attribute values, or is constrained by a given hierarchy or taxonomy. Pattern structures convey important knowledge about the patterns. In this paper, we present a novel architecture that organizes the mining space based on pattern structures. By exploiting the inter-relationships among pattern structures, execution times for mining can be reduced significantly. This advantage is demonstrated by our experiments using both synthetic and real-life datasets. © 2002 IEEE.