论文标题
CICLAD：流的快速和记忆效率的封闭项目集矿工
CICLAD: A Fast and Memory-efficient Closed Itemset Miner for Streams
论文作者
论文摘要
来自数据流的采矿协会规则是一项具有挑战性的任务，因为（通常）可用的资源有限，而结果的大尺寸。频繁的封闭项目集（FCI）实现了有效的第一步，但是当前的FCI流矿工并不是资源消耗的最佳选择，例如他们以额外的费用存储大量额外的商品集。在寻找更好的存储效率折衷时，我们设计了CICLAD，这是一种基于相交的滑动窗口FCI矿工。利用对FCI演化的深入了解，它结合了最小的存储空间与快速访问。实验结果表明，Ciclad的记忆印迹要低得多，并且其表现在全球范围内比竞争对手方法更好。
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.
