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.
Type de document :
Communication dans un congrès
IEEE International Conference on Data Mining, Dec 2013, Dallas, United States. 2013
Liste complète des métadonnées

https://hal.inria.fr/hal-00922255
Contributeur : Chedy Raïssi <>
Soumis le : mercredi 25 décembre 2013 - 11:21:52
Dernière modification le : jeudi 19 avril 2018 - 14:38:06

Identifiants

  • HAL Id : hal-00922255, version 1

Citation

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. 2013. 〈hal-00922255〉

Partager

Métriques

Consultations de la notice

401