CICLAD: A Fast and Memory-efficient Closed Itemset Miner for Streams
This work addresses resource efficiency for data stream mining applications, though it appears incremental as it builds on existing frequent closed itemset techniques.
The paper tackled the problem of mining frequent closed itemsets from data streams with high resource consumption by introducing Ciclad, an intersection-based sliding-window miner, which achieved much lower memory usage and better overall performance compared to existing methods.
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.