Data Access Paths for Frequent Itemsets Discovery

Abstract

Many frequent itemset discovery algorithms have been proposed in the area of data mining research. The algorithms exhibit significant computational complexity, resulting in long processing times. Their performance is also dependent on source data characteristics. We argue that users should not be responsible for choosing the most efficient algorithm to solve a particular data mining problem. Instead, a data mining query optimizer should follow the costbased optimization rules to select the appropriate method to solve the user’s problem. The optimizer should consider alternative data mining algorithms as well as alternative data access paths. In this paper, we use the concept of materialized views to describe possible data access paths for frequent itemset discovery.