摘要翻译：
一种更好地查看数据的方法是使用频繁模式。在本文中，频繁模式是在项目集流中出现次数最少的子集。然而，在流中频繁模式的发现一直是有问题的。因为流可能是无穷无尽的，所以原则上不可能判断一个模式是否经常出现。此外，模式的数量可能很大，而且流结构的良好概述很快就会丢失。所提出的方法将使用聚类来促进流结构的分析。对模式共现的聚类将使用户对流的结构有一个改进的视图。一些模式可能经常出现在一起，以至于它们应该形成一个组合模式。这样，聚类中的模式将是最大频繁模式：最大频繁模式。当只知道成对之间的距离时，我们决定模式是否经常一起出现的方法将基于聚类方法。最大频繁模式的数目要少得多，并且与聚类方法相结合，这些模式提供了对流结构的良好视图。
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英文标题：
《Clustering Co-occurrence of Maximal Frequent Patterns in Streams》
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作者：
Edgar H. de Graaf, Joost N. Kok, Walter A. Kosters
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Data Structures and Algorithms        数据结构与算法
分类描述：Covers data structures and analysis of algorithms. Roughly includes material in ACM Subject Classes E.1, E.2, F.2.1, and F.2.2.
涵盖数据结构和算法分析。大致包括ACM学科类E.1、E.2、F.2.1和F.2.2中的材料。
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英文摘要：
  One way of getting a better view of data is using frequent patterns. In this paper frequent patterns are subsets that occur a minimal number of times in a stream of itemsets. However, the discovery of frequent patterns in streams has always been problematic. Because streams are potentially endless it is in principle impossible to say if a pattern is often occurring or not. Furthermore the number of patterns can be huge and a good overview of the structure of the stream is lost quickly. The proposed approach will use clustering to facilitate the analysis of the structure of the stream.   A clustering on the co-occurrence of patterns will give the user an improved view on the structure of the stream. Some patterns might occur so much together that they should form a combined pattern. In this way the patterns in the clustering will be the largest frequent patterns: maximal frequent patterns.   Our approach to decide if patterns occur often together will be based on a method of clustering when only the distance between pairs is known. The number of maximal frequent patterns is much smaller and combined with clustering methods these patterns provide a good view on the structure of the stream.
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PDF链接：
https://arxiv.org/pdf/0705.0588