A. Giacometti, Dominique H. Li, Patrick Marcel, Arnaud Soulet
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引用次数: 29
摘要
1993年,Rakesh Agrawal, Tomasz Imielinski和Arun N. Swami发表了模式挖掘的创始论文之一:“挖掘大型数据库中项目集之间的关联规则”。除了介绍一个新问题之外,它还介绍了解决和评估方面的新方法。二十年来,模式挖掘一直是数据库知识发现中最活跃的领域之一。本文对KDD、PKDD、PAKDD、ICDM和SDM五大国际会议的1087篇论文进行文献计量调查。我们首先测量了在KDD领域持续增长的同时,致力于模式挖掘的研究的放缓。然后,我们量化了语言,约束和浓缩表示方面的主要贡献,以概述当前的方向。在过去的20年里,我们观察到语言的复杂性,尽管关联规则和项集是迄今为止研究最多的。正如预期的那样,最小支持约束在大约50%的出版物中主导了模式的提取。最后,在2005年至2008年期间,10%的论文中使用的浓缩表示相对成功。
In 1993, Rakesh Agrawal, Tomasz Imielinski and Arun N. Swami published one of the founding papers of Pattern Mining: "Mining Association Rules between Sets of Items in Large Databases". Beyond the introduction to a new problem, it introduced a new methodology in terms of resolution and evaluation. For two decades, Pattern Mining has been one of the most active fields in Knowledge Discovery in Databases. This paper provides a bibliometric survey of the literature relying on 1,087 publications from five major international conferences: KDD, PKDD, PAKDD, ICDM and SDM. We first measured a slowdown of research dedicated to Pattern Mining while the KDD field continues to grow. Then, we quantified the main contributions with respect to languages, constraints and condensed representations to outline the current directions. We observe a sophistication of languages over the last 20 years, although association rules and itemsets are so far the most studied ones. As expected, the minimal support constraint predominates the extraction of patterns with approximately 50% of the publications. Finally, condensed representations used in 10% of the papers had relative success particularly between 2005 and 2008.