高效挖掘压缩重复间隔序列模式
Mining Compressed Repetitive Gapped Sequential Patterns Efficiently
摘要：
从序列数据库中挖掘频繁序列模式一直是数据采掘的一个研究热点，人们提出并研究了各种有效的序列模式挖掘算法。近年来，在许多问题领域（如程序执行轨迹）中，一种新的序列模式挖掘方法--重复间隔序列模式挖掘引起了许多研究者的关注，它不仅考虑了序列模式在不同序列中的重复，而且考虑了序列内的重复，这比一般的序列模式挖掘方法更有意义。然而，即使这些封闭挖掘算法产生的重复间隔序列模式的数量可能太大，用户无法理解，特别是当支持度阈值较低时。在本文中，我们提出并研究了重复间隔序列模式的压缩问题。在总结频繁项集的思想RPglobal的启发下，我们提出了一个算法CRGSgrow（Compressing Repetitive Gapped Sequential pattern grow），包括一个有效的剪枝策略SyncScan和一个有效的代表模式检测方案--dominant sequential pattern checking。CRGSgrow是一个两步的方法：第一步，我们获得所有封闭的重复序列模式作为代表性重复序列模式的候选集，同时得到最多的代表性重复序列模式;第二步，我们只花很少的时间从候选集中找到剩余的代表性模式。对真实数据集和合成数据集的实证研究清楚地表明，CRGSgrow具有良好的性能。
英文摘要：
Mining frequent sequential patterns from sequence databases has been a central research topic in data mining and various efficient mining sequential patterns algorithms have been proposed and studied. Recently, in many problem domains (e.g, program execution traces), a novel sequential pattern mining research, called mining repetitive gapped sequential patterns, has attracted the attention of many researchers, considering not only the repetition of sequential pattern in different sequences but also the repetition within a sequence is more meaningful than the general sequential pattern mining which only captures occurrences in different sequences. However, the number of repetitive gapped sequential patterns generated by even these closed mining algorithms may be too large to understand for users, especially when support threshold is low. In this paper, we propose and study the problem of compressing repetitive gapped sequential patterns. Inspired by the ideas of summarizing frequent itemsets, RPglobal, we develop an algorithm, CRGSgrow (Compressing Repetitive Gapped Sequential pattern grow), including an efficient pruning strategy, SyncScan, and an efficient representative pattern checking scheme, -dominate sequential pattern checking. The CRGSgrow is a two-step approach: in the first step, we obtain all closed repetitive sequential patterns as the candidate set of representative repetitive sequential patterns, and at the same time get the most of representative repetitive sequential patterns; in the second step, we only spend a little time in finding the remaining the representative patterns from the candidate set. An empirical study with both real and synthetic data sets clearly shows that the CRGSgrow has good performance.
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