The Australasian Data Mining Workshop Copyright 2002 Building a Data Mining Query Optimizer

In this paper, we describe our research into building an optimizer for association rule queries. We present a framework for the query processor and report on the progress of our research so far. An extended SQL syntax is used for expressing association rule queries, and query trees of operators in an extended relational algebra for their internal representation. The placement of constraints in the query tree is discussed. We have developed an efficient algorithm called CT-ITL for lower level implementation of frequent item set generation which is the most critical step of association rule mining. The performance evaluations show that our algorithm compares well with the most efficient algorithms available currently. We also discuss further steps needed to reach our goal of integrating the optimizer with database systems. Keywords Knowledge discovery and data mining, query optimization, association rules, frequent item sets. 1