CICLAD: A Fast and Memory-efficient Closed Itemset Miner for Streams

TL;DR

CICLAD is a novel stream mining algorithm that efficiently finds frequent closed itemsets using minimal memory and fast access, outperforming existing methods in resource use and speed.

Contribution

It introduces CICLAD, an intersection-based sliding-window FCI miner that improves storage efficiency and performance over prior stream mining algorithms.

Findings

CICLAD uses significantly less memory than competitors.

CICLAD achieves faster processing times.

CICLAD maintains high accuracy in FCI detection.

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.