An Adaptive Gradual Relational Association Rules Mining Approach
This paper focuses on adaptive Gradual Relational Association Rules mining. Gradual Relational Association Rules capture gradual generic relations among data features. We propose AGRARM, an algorithm for mining the interesting Gradual Relational Association Rules characterizing a data set that has been extended with a number of new attributes, through adapting the set of interesting rules mined before extension, so as to preserve the completeness. We aim, through AGRARM, to make the mining process more efficient than resuming the mining algorithm on the enlarged data. We have experimentally evaluated AGRARM versus mining from scratch on three publicly available data sets. The obtained reduction in mining time highlights AGRARM’s efficiency, thus confirming the potential of our proposal.
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