Title:A Seamless Integration of Association Rule Mining with Database Systems

Abstract: The need for Knowledge and Data Discovery Management Systems (KDDMS) that support ad hoc data mining queries has been long recognized. A significant amount of research has gone into building tightly coupled systems that integrate association rule mining with database systems. In this paper, we describe a seamless integration scheme for database queries and association rule discovery using a common query optimizer for both. Query trees of expressions in an extended algebra are used for internal representation in the optimizer. The algebraic representation is flexible enough to deal with constrained association rule queries and other variations of association rule specifications. We propose modularization to simplify the query tree for complex tasks in data mining. It paves the way for making use of existing algorithms for constructing query plans in the optimization process. How the integration scheme we present will facilitate greater user control over the data mining process is also discussed. The work described in this paper forms part of a larger project for fully integrating data mining with database management.