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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51833

    Title: Discovering multi-label temporal patterns in sequence databases
    Authors: Chen,YL;Wu,SY;Wang,YC
    Contributors: 資訊管理學系
    Date: 2011
    Issue Date: 2012-03-27 19:07:18 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 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. (C) 2010 Elsevier Inc. All rights reserved.
    Appears in Collections:[資訊管理學系] 期刊論文

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