Efficient mining of frequent episodes from complex sequences

Kuo Yu Huang, Chia Hui Chang

研究成果: 雜誌貢獻期刊論文同行評審

71 引文 斯高帕斯(Scopus)

摘要

Discovering patterns with great significance is an important problem in data mining discipline. An episode is defined to be a partially ordered set of events for consecutive and fixed-time intervals in a sequence. Most of previous studies on episodes consider only frequent episodes in a sequence of events (called simple sequence). In real world, we may find a set of events at each time slot in terms of various intervals (hours, days, weeks, etc.). We refer to such sequences as complex sequences. Mining frequent episodes in complex sequences has more extensive applications than that in simple sequences. In this paper, we discuss the problem on mining frequent episodes in a complex sequence. We extend previous algorithm MINEPI to MINEPI + for episode mining from complex sequences. Furthermore, a memory-anchored algorithm called EMMA is introduced for the mining task. Experimental evaluation on both real-world and synthetic data sets shows that EMMA is more efficient than MINEPI +.

原文???core.languages.en_GB???
頁(從 - 到)96-114
頁數19
期刊Information Systems
33
發行號1
DOIs
出版狀態已出版 - 3月 2008

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