The problem of discovering frequent itemsets including rare ones has received a great deal of attention. The mining process needs to be flexible enough to extract frequent and rare regularities at once. On the other hand, it has recently been shown that constraint programming is a flexible way to tackle data mining tasks. In this paper, we propose a constraint programming approach for mining itemsets with multiple minimum supports. Our approach provides the user with the possibility to express any kind of constraints on the minimum item supports. An experimental analysis shows the practical effectiveness of our approach compared to the state of the art.

中文翻译：

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具有多个最小支持度的频繁项集挖掘：一种基于约束的方法

发现包括稀有项在内的频繁项集的问题受到了广泛关注。挖掘过程需要足够灵活，以一次提取频繁和罕见的规律。另一方面，最近已经表明约束规划是处理数据挖掘任务的灵活方法。在本文中，我们提出了一种用于挖掘具有多个最小支持度的项集的约束规划方法。我们的方法为用户提供了在最小项目支持上表达任何类型的约束的可能性。实验分析表明，与现有技术相比，我们的方法的实际有效性。