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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29655


    Title: Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events
    Authors: Wu,SY;Chen,YL
    Contributors: 資訊管理研究所
    Keywords: MINING SEQUENTIAL PATTERNS;LARGE DATABASES;KNOWLEDGE;GROWTH;MAINTENANCE;PREFIXSPAN
    Date: 2009
    Issue Date: 2010-06-29 20:37:28 (UTC+8)
    Publisher: 中央大學
    Abstract: Previous sequential pattern mining studies have dealt with either point-based event sequences or interval-based event sequences. In some applications, however, event sequences may contain both point-based and interval-based events. These sequences are called hybrid event sequences. Since the relationships among both kinds of events are more diversiform, the information obtained by discovering patterns from these events is more informative. In this study we introduce a hybrid temporal pattern mining problem and develop an algorithm to discover hybrid temporal patterns from hybrid event sequences. We carry out an experiment using both synthetic and real stock price data to compare our algorithm with the traditional algorithms designed exclusively for mining point-based patterns or interval-based patterns. The experimental results indicate that the efficiency of our algorithm is satisfactory. In addition, the experiment also shows that the predicting power of hybrid temporal patterns is higher than that of point-based or interval-based patterns. (C) 2009 Elsevier B.V. All rights reserved.
    Relation: DATA & KNOWLEDGE ENGINEERING
    Appears in Collections:[資訊管理研究所] 期刊論文

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