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Conference Papers Year : 2013

Mining Statistically Significant Sequential Patterns

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Cécile Low-Kam
  • Function : Author
  • PersonId : 991375
Chedy Raïssi
Mehdi Kaytoue
Jian Pei
  • Function : Author
  • PersonId : 906319


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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Dates and versions

hal-00922255 , version 1 (25-12-2013)


  • HAL Id : hal-00922255 , version 1


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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