376 research outputs found
CICLAD: A Fast and Memory-efficient Closed Itemset Miner for Streams
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.Comment: KDD2
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