Discovering multi-label temporal patterns in sequence databases

Yen Liang Chen, Shin Yi Wu, Yu Cheng Wang

研究成果: 雜誌貢獻期刊論文同行評審

20 引文 斯高帕斯(Scopus)


Sequential pattern mining is one of the most important data mining techniques. Previous research on mining sequential patterns discovered patterns from point-based event data, interval-based event data, and hybrid event data. In many real life applications, however, an event may involve many statuses; it might not occur only at one certain point in time or over a period of time. In this work, we propose a generalized representation of temporal events. We treat events as multi-label events with many statuses, and introduce an algorithm called MLTPM to discover multi-label temporal patterns from temporal databases. The experimental results show that the efficiency and scalability of the MLTPM algorithm are satisfactory. We also discuss interesting multi-label temporal patterns discovered when MLTPM was applied to historical Nasdaq data.

頁(從 - 到)398-418
期刊Information Sciences
出版狀態已出版 - 1 2月 2011


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