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

Shin Yi Wu, Yen Liang Chen

研究成果: 雜誌貢獻期刊論文同行評審

32 引文 斯高帕斯(Scopus)

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.

原文???core.languages.en_GB???
頁(從 - 到)1309-1330
頁數22
期刊Data and Knowledge Engineering
68
發行號11
DOIs
出版狀態已出版 - 11月 2009

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