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.
quantitative association rule;
机译:基于粒子群算法的频繁项与不频繁项关联规则挖掘
机译:Tcom,一种用于在不频繁项目之间挖掘关联规则的创新数据结构
机译:频繁和不频繁项目之间的有效关联规则挖掘
机译:不频繁项之间的关联规则和定量关联规则挖掘
机译:罕见项目之间的关联规则挖掘和定量关联规则挖掘。
机译:使用频繁和不频繁项集从文本中挖掘负向和正向关联规则
机译:TCOM,一种用于在不频繁项目之间挖掘关联规则的创新数据结构