摘要翻译：
从序列数据库中挖掘频繁序列模式一直是数据挖掘的核心研究课题，各种高效的序列模式挖掘算法被提出和研究。近年来，在许多问题领域（如程序执行轨迹）中，一种新颖的序列模式挖掘研究引起了许多研究者的关注，它不仅考虑到序列模式在不同序列中的重复，而且考虑到序列内的重复比一般的序列模式挖掘只捕获不同序列中的事件更有意义。然而，即使是这些封闭的挖掘算法产生的重复间隙序列模式的数量也可能太多，用户无法理解，尤其是在支持阈值较低的情况下。本文提出并研究了压缩重复间隙序列模式的问题。在总结频繁项集RPglobal思想的启发下，我们提出了一种算法CRGSgrow（Compressing repletitive Gapped Sequential pattern grown）,其中包括一种高效的剪枝策略SyncScan和一种高效的代表性模式检查方案-支配序列模式检查。CRGSgrow算法分两步进行：第一步，获取所有闭合的重复序列模式作为有代表性的重复序列模式候选集，同时得到最有代表性的重复序列模式；在第二步中，我们只花很少的时间从候选集中寻找剩余的代表性模式。通过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。
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英文标题：
《Mining Compressed Repetitive Gapped Sequential Patterns Efficiently》
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作者：
Yongxin Tong, Li Zhao, Dan Yu, Shilong Ma, Ke Xu
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Databases        数据库
分类描述：Covers database management, datamining, and data processing. Roughly includes material in ACM Subject Classes E.2, E.5, H.0, H.2, and J.1.
涵盖数据库管理、数据挖掘和数据处理。大致包括ACM学科类E.2、E.5、H.0、H.2和J.1中的材料。
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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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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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PDF链接：
https://arxiv.org/pdf/0906.0885