Prism:一种用于频繁序列挖掘的原始编码方法

Seventh IEEE International Conference on Data Mining (ICDM 2007) Pub Date : 2007-10-28 DOI:10.1109/ICDM.2007.33

K. Gouda, M. Hassaan, Mohammed J. Zaki

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引用次数: 49

摘要

序列挖掘是数据挖掘的基本任务之一。在本文中，我们提出了一种新的方法，称为Prism，用于挖掘频繁序列。Prism利用垂直方法进行枚举和支持计数，该方法基于新颖的0 /素数块编码概念，而该概念又基于素数分解理论。通过对合成和真实数据集的广泛评估，我们表明Prism优于流行的序列挖掘方法，如SPADE [10]， PrefixSpan[6]和SPAM[2]，高出一个数量级或更多。

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Prism: A Primal-Encoding Approach for Frequent Sequence Mining

Sequence mining is one of the fundamental data mining tasks. In this paper we present a novel approach called Prism, for mining frequent sequences. Prism utilizes a vertical approach for enumeration and support counting, based on the novel notion o/prime block encoding, which in turn is based on prime factorization theory. Via an extensive evaluation on both synthetic and real datasets, we show that Prism outperforms popular sequence mining methods like SPADE [10], PrefixSpan [6] and SPAM [2], by an order of magnitude or more.

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Seventh IEEE International Conference on Data Mining (ICDM 2007)

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