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.

中文翻译：

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CICLAD：用于流的快速且内存高效的封闭项集挖掘器

由于（通常）有限的可用资源与大尺寸的结果，从数据流中挖掘关联规则是一项具有挑战性的任务。频繁闭合项集 (FCI) 实现了高效的第一步，但当前的 FCI 流挖掘器在资源消耗方面并不是最优的，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，一种基于交叉点的滑动窗口 FCI 矿工。利用对 FCI 演变的深入洞察，它将最小存储与快速访问相结合。实验结果表明，Ciclad 的记忆印记要低得多，而且它的整体性能比竞争对手的方法好。