Abstract
This paper proposes a new approach to mine multirelational databases. Our approach is based on the representation of a multirelational database as a set of trees. Tree mining techniques can then be applied to identify frequent patterns in this kind of databases. We propose two alternative schemes for representing a multirelational database as a set of trees. The frequent patterns that can be identified in such set of trees can be used as the basis for other multirelational data mining techniques, such as association rules, classification, or clustering.
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© 2009 Springer-Verlag Berlin Heidelberg
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Jiménez, A., Berzal, F., Cubero, JC. (2009). Frequent Itemset Mining in Multirelational Databases. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds) Foundations of Intelligent Systems. ISMIS 2009. Lecture Notes in Computer Science(), vol 5722. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04125-9_5
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DOI: https://doi.org/10.1007/978-3-642-04125-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04124-2
Online ISBN: 978-3-642-04125-9
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