Randomly sampling maximal itemsets

Sandy Moens, Bart Goethals
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引用次数: 23

Abstract

Pattern mining techniques generally enumerate lots of uninteresting and redundant patterns. To obtain less redundant collections, techniques exist that give condensed representations of these collections. However, the proposed techniques often rely on complete enumeration of the pattern space, which can be prohibitive in terms of time and memory. Sampling can be used to filter the output space of patterns without explicit enumeration. We propose a framework for random sampling of maximal itemsets from transactional databases. The presented framework can use any monotonically decreasing measure as interestingness criteria for this purpose. Moreover, we use an approximation measure to guide the search for maximal sets to different parts of the output space. We show in our experiments that the method can rapidly generate small collections of patterns with good quality. The sampling framework has been implemented in the interactive visual data mining tool called MIME1, as such enabling users to quickly sample a collection of patterns and analyze the results.
随机抽样最大项目集
模式挖掘技术通常会列举出大量无趣和冗余的模式。为了获得较少冗余的集合,存在提供这些集合的浓缩表示的技术。然而,建议的技术通常依赖于模式空间的完整枚举,这在时间和内存方面可能是令人望而却步的。采样可以用来过滤模式的输出空间,而不需要显式枚举。我们提出了一个从事务性数据库中随机抽取最大项集的框架。提出的框架可以使用任何单调递减的度量作为兴趣度标准。此外,我们使用近似度量来指导搜索输出空间的不同部分的最大集合。实验表明,该方法可以快速生成质量良好的小块图案集合。采样框架已经在交互式可视化数据挖掘工具MIME1中实现,这样用户就可以快速采样一组模式并分析结果。
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