Abstract
Mining frequent queries often requires the repeated execution of some extraction algorithm for different values of the support, as well as for different source datasets. This is an expensive process, even if we use the best existing algorithms. Hence the need for iterative mining, whereby mining results already obtained are re-used to accelerate subsequent steps in the mining process.
In this paper, we present an approach for the iterative mining of frequent queries. Our approach is based on the notion of mining context, where a mining context is a set of queries over the same schema. We define operations on mining contexts, based on the standard relational algebra, and we also introduce new operators, one of which for computing frequent queries.
We first study the properties of the operators, then we consider particular mining contexts using biases for which frequent queries can be computed using any level-wise algorithm. Iterative mining is obtained by combining these particular contexts using our set of operations. We have implemented our approach and conducted experiments that show its efficiency in mining frequent queries.
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Diop, C.T., Giacometti, A., Laurent, D., Spyratos, N. (2006). Computation of Mining Queries: An Algebraic Approach. In: Boulicaut, JF., De Raedt, L., Mannila, H. (eds) Constraint-Based Mining and Inductive Databases. Lecture Notes in Computer Science(), vol 3848. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11615576_6
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DOI: https://doi.org/10.1007/11615576_6
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