Association rule mining among frequent items has been extensively studied in data mining research. However, in the recent years, there is an increasing demand of mining the infrequent items (such as rare but expensive items). Since exploring interesting relationship among infrequent items has not been discussed much in the literature, in this paper, we propose two simple, practical and effective schemes to mine association rules among rare items. Our algorithm can also be applied to frequent items with bounded length. Experiments are performed on the well-known IBM synthetic database. Our schemes compare favorably to Apriori and FP-growth under the situation being evaluated. In addition, we explore quantitative association rule mining in transactional database among infrequent items by associating quantities of items purchased; some interesting examples are drawn to illustrate the significance of such mining.
在数据挖掘研究中已经广泛研究了频繁项目之间的关联规则挖掘。然而,近年来,对稀有物品(例如稀有但昂贵的物品)的开采需求增加。由于在文献中很少探讨稀有项目之间的有趣关系,因此,本文提出了两种简单,实用和有效的方案来挖掘稀有项目之间的关联规则。我们的算法也可以应用于具有限制长度的频繁项。实验是在著名的IBM综合数据库上执行的。在所评估的情况下,我们的方案优于Apriori和FP-growth。此外,我们通过关联购买的商品数量来探索交易数据库中不频繁商品之间的定量关联规则挖掘;列举了一些有趣的例子来说明这种挖掘的重要性。 P>
quantitative association rule;
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