Abstract
Association rule mining often requires the repeated execution of some extraction algorithm for different values of the support and confidence thresholds, as well as for different source datasets. This is an expensive process, even if we use the best existing algorithms. Hence the need for incremental mining, whereby mining results already obtained can be used to accelerate subsequent steps in the mining process.
In this paper, we present an approach for the incremental mining of multidimensional association rules. In our approach, association rule mining takes place in a mining context which specifies the form of rules to be mined. Incremental mining is obtained by combining mining contexts using relational algebra operations.
Work by this author was conducted, in part, while visiting at the National Technical University of Athens, Greece, under a Pened/Geget project.
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Diop, C.T., Giacometti, A., Laurent, D., Spyratos, N. (2002). Composition of Mining Contexts for Efficient Extraction of Association Rules. In: Jensen, C.S., et al. Advances in Database Technology — EDBT 2002. EDBT 2002. Lecture Notes in Computer Science, vol 2287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45876-X_9
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DOI: https://doi.org/10.1007/3-540-45876-X_9
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