Rule Discovery using Patterns from Joined Table of Relational Databases

Several problems exist for data mining in real world databases: the difficulty of determining a decision attribute when limited domain knowledge exists, the difficulty in selecting a decision attribute from a new table formed from joining several relations, and the problem of elaborate data selection in the knowledge discovery process. This paper presents algorithms to solve these problems using methods that 1) determine a good decision attribute based on an approach developed from rough set theory and decision tree generation and 2) find meaningful frequent patterns based on attributes and dependencies in relational databases which are used to cluster values and generate tables. Moreover, our methods take advantage of the fact that more general concepts occur more frequently, making stepwise refinement possible.