Rule synthesizing from multiple related databases
- Publication Type:
- Conference Proceeding
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6119 LNAI (PART 2), pp. 201 - 213
- Issue Date:
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
In this paper, we study the problem of rule synthesizing from multiple related databases where items representing the databases may be different, and the databases may not be relevant, or similar to each other. We argue that, for such multi-related databases, simple rule synthesizing without a detailed understanding of the databases is not able to reveal meaningful patterns inside the data collections. Consequently, we propose a two-step clustering on the databases at both item and rule levels such that the databases in the final clusters contain both similar items and similar rules. A weighted rule synthesizing method is then applied on each such cluster to generate final rules. Experimental results demonstrate that the new rule synthesizing method is able to discover important rules which can not be synthesized by other methods. © 2010 Springer-Verlag Berlin Heidelberg.
Please use this identifier to cite or link to this item: