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Discovering multi-label temporal patterns in sequence databases

Discovering multi-label temporal patterns in sequence databases,10.1016/j.ins.2010.09.024,Information Sciences,Yen-Liang Chen,Shin-yi Wu,Yu-Cheng Wang

Discovering multi-label temporal patterns in sequence databases   (Citations: 4)
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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.
Journal: Information Sciences - ISCI , vol. 181, no. 3, pp. 398-418, 2011
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