Discovering time-interval sequential patterns in sequence databases

Yen Liang Chen, Mei Ching Chiang, Ming Tat Ko

研究成果: 雜誌貢獻期刊論文同行評審

124 引文 斯高帕斯(Scopus)

摘要

Sequential pattern mining, which discovers frequent subsequences as patterns in a sequence database, in an important data-mining problem with broad applications. Although conventional sequential patterns can reveal the order of items, the time between items is not determined; that is, a sequential pattern does not include time intervals between successive items. Accordingly, this work addresses sequential patterns that include time intervals, called time-interval sequential patterns. This work develops two efficient algorithms for mining time-interval sequential patterns. The first algorithm is based on the conventional Apriori algorithm, while the second one is based on the PrefixSpan algorithm. The latter algorithm outperforms the former, not only in computing time but also in scalability with respect to various parameters.

原文???core.languages.en_GB???
頁(從 - 到)343-354
頁數12
期刊Expert Systems with Applications
25
發行號3
DOIs
出版狀態已出版 - 10月 2003

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