Abstract
Mining association rule in event sequences is an important data mining problem with many applications. Most of previous studies on association rules are on mining intra-transaction association, which consider only relationship among the item in the same transaction. However, intra-transaction association rules are not a suitable for trend prediction. Therefore, inter-transaction association is introduced, which consider the relationship among itemset of multiple time instants. In this paper, we present PROWL, an efficient algorithm for mining inter-transaction rules. By using projected window method and depth first enumeration approach, we can discover all frequent patterns quickly. Finally, an extensive experimental evaluation on a number of real and synthetic database shows that PROWL significantly outperforms previous method.
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© 2004 Springer-Verlag Berlin Heidelberg
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Huang, KY., Chang, CH., Lin, KZ. (2004). PROWL: An Efficient Frequent Continuity Mining Algorithm on Event Sequences. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2004. Lecture Notes in Computer Science, vol 3181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30076-2_35
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DOI: https://doi.org/10.1007/978-3-540-30076-2_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22937-7
Online ISBN: 978-3-540-30076-2
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