Abstract
The proliferation of information sources available on the Wide World Web has resulted in a need for database selection tools to locate the potential useful information sources with respect to the user’s information need. Current database selection tools always treat each database independently, ignoring the implicit, useful associations between distributed databases. To overcome this shortcoming, in this paper, we introduce a data-mining approach to assist the process of database selection by extracting potential interesting association rules between web databases from a collection of previous selection results. With a topic hierarchy, we exploit intraclass and interclass associations between distributed databases, and use the discovered knowledge on distributed databases to refine the original selection results. We present experimental results to demonstrate that this technique is useful in improving the effectiveness of database selection.
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© 2004 Springer-Verlag Berlin Heidelberg
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Yang, H., Zhang, M., Shi, Z. (2004). Association-Rule Based Information Source Selection. In: Zhang, C., W. Guesgen, H., Yeap, WK. (eds) PRICAI 2004: Trends in Artificial Intelligence. PRICAI 2004. Lecture Notes in Computer Science(), vol 3157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28633-2_60
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DOI: https://doi.org/10.1007/978-3-540-28633-2_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22817-2
Online ISBN: 978-3-540-28633-2
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