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Efficient mining of sequential patterns with time constraints by delimited pattern growth

机译：通过有限的模式增长有效地挖掘具有时间限制的顺序模式

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An active research topic in data mining is the discovery of sequential patterns, which finds all frequent subsequences in a sequence database. The generalized sequential pattern (GSP) algorithm was proposed to solve the mining of sequential patterns with time constraints, such as time gaps and sliding time windows. Recent studies indicate that the pattern-growth methodology could speed up sequence mining. However, the capabilities to mine sequential patterns with time constraints were previously available only within the Apriori framework. Therefore, we propose the DELISP (delimited sequential pattern) approach to provide the capabilities within the pattern-growth methodology. DELISP features in reducing the size of projected databases by bounded and windowed projection techniques. Bounded projection keeps only time-gap valid subsequences and windowed projection saves nonredundant subsequences satisfying the sliding time-window constraint. Furthermore, the delimited growth technique directly generates constraint-satisfactory patterns and speeds up the pattern growing process. The comprehensive experiments conducted show that DELISP has good scalability and outperforms the well-known GSP algorithm in the discovery of sequential patterns with time constraints.
机译：数据挖掘中一个活跃的研究主题是序列模式的发现，该序列模式可在序列数据库中找到所有常见的子序列。提出了广义序列模式（GSP）算法，以解决具有时间限制（例如时间间隔和滑动时间窗口）的序列模式的挖掘。最近的研究表明，模式增长方法可以加快序列挖掘的速度。但是，以前只有在Apriori框架内才可以使用具有时间限制的顺序模式进行挖掘。因此，我们提出了DELISP（定界顺序模式）方法来提供模式增长方法中的功能。 DELISP通过有界和窗口化投影技术来减少投影数据库的大小。有界投影仅保留有时间间隔的有效子序列，而开窗投影则保留满足滑动时间窗约束的非冗余子序列。此外，定界增长技术直接生成约束令人满意的模式，并加快了模式增长过程。进行的综合实验表明，DELISP具有良好的可扩展性，并且在发现具有时间限制的顺序模式时，其性能优于著名的GSP算法。

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