Mining Statistically Significant Sequential Patterns

Cécile Low-Kam 1 Chedy Raïssi 2 Mehdi Kaytoue 3 Jian Pei 4
2 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
3 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Recent developments in the frequent pattern mining framework uses additional measures of interest to reduce the set of discovered patterns. We introduce a rigorous and efficient approach to mine statistically significant, unexpected patterns in sequences of itemsets. The proposed methodology is based on a null model for sequences and on a multiple testing procedure to extract patterns of interest. Experiments on sequences of replays of a video game demonstrate the scalability and the efficiency of the method to discover unexpected game strategies.
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Conference papers
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https://hal.inria.fr/hal-00922255
Contributor : Chedy Raïssi <>
Submitted on : Wednesday, December 25, 2013 - 11:21:52 AM
Last modification on : Wednesday, April 17, 2019 - 12:21:21 PM

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  • HAL Id : hal-00922255, version 1

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Cécile Low-Kam, Chedy Raïssi, Mehdi Kaytoue, Jian Pei. Mining Statistically Significant Sequential Patterns. IEEE International Conference on Data Mining, Dec 2013, Dallas, United States. ⟨hal-00922255⟩

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