Likely-Occurring Itemsets for Pattern Mining

We consider the itemset mining problem in general settings, e.g., mining association rules and itemset selection. We introduce the notion of likely-occurring itemsets and propose a greedy approach to itemset search space discovery that allows for reducing the number of arbitrary or closed itemsets. This method provides itemsets that are useful for different objectives and can be used as an additional constraint to curb the itemset explosion. In experiments, we show that the method is useful both for compression-based itemset mining and for computing good-quality association rules.

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