23/08/2020

CICLAD: A fast and memory-efficient closed itemset miner for streams

Tomas Martin, Guy Francoeur, Petko Valtchev

Keywords: pattern mining, closed itemsets, unsupervised learning, data streams

Abstract Paper Similar Papers

Abstract: Mining association rules from data streams is a challenging task due to the (typically) limited resources available vs. the large size of the result. Frequent closed itemsets (FCI) enable an efficient first step, yet current FCI stream miners are not optimal on resource consumption, e.g. they store a large number of extra itemsets at an additional cost. In a search for a better storage-efficiency trade-off, we designed Ciclad, an intersection-based sliding-window FCI miner. Leveraging in-depth insights into FCI evolution, it combines minimal storage with quick access. Experimental results indicate Ciclad’s memory imprint is much lower and its performances globally better than competitor methods.

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