流中最大频繁模式的聚类共生
Clustering Co-occurrence of Maximal Frequent Patterns in Streams
摘要：
更好地查看数据的一种方法是使用频繁模式。在本文中，频繁模式是指在一个项集中出现次数最少的子集。然而，流中频繁模式的发现一直是有问题的。因为流可能是无止境的，所以原则上不可能说一个模式是否经常发生。此外，模式的数量可能是巨大的，并且流的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的结构的分析。对模式同现的聚类将为用户提供关于流结构的改进视图。有些模式可能会同时出现，因此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。我们的方法来决定，如果模式经常出现在一起，将基于一种方法的聚类时，只有对之间的距离是已知的。最大频繁模式的数量要小得多，结合聚类方法，这些模式提供了一个很好的视图上的流的结构。
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
