CICLAD：一个快速高效的流闭项集挖掘器
CICLAD: A Fast and Memory-efficient Closed Itemset Miner for Streams
摘要：
从数据流中挖掘关联规则是一项具有挑战性的任务，因为（通常）可用的资源有限，而结果的大小很大。频繁闭项集（FCI）实现了有效的第一步，然而当前的FCI流挖掘器在资源消耗上不是最优的，例如，它们以额外的成本存储大量额外的项集。为了寻求更好的存储效率权衡，我们设计了Ciclad，一个基于交集的滑动窗口FCI矿工。利用对FCI发展的深入见解，它将最小存储与快速访问相结合。实验结果表明，Ciclad的记忆印记低得多，其性能优于竞争对手的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
