Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events

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題名:	Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events
作者:	Wu,SY;Chen,YL
貢獻者:	資訊管理研究所
關鍵詞:	MINING SEQUENTIAL PATTERNS;LARGE DATABASES;KNOWLEDGE;GROWTH;MAINTENANCE;PREFIXSPAN
日期:	2009
上傳時間:	2010-06-29 20:37:28 (UTC+8)
出版者:	中央大學
摘要:	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.
關聯:	DATA & KNOWLEDGE ENGINEERING
顯示於類別:	[資訊管理研究所] 期刊論文

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