Mining Statistically Significant Sequential Patterns - Archive ouverte HAL Access content directly
Conference Papers Year : 2013

Mining Statistically Significant Sequential Patterns

(1) , (2) , (3) , (4)
1
2
3
4
Cécile Low-Kam
  • Function : Author
  • PersonId : 991375
Chedy Raïssi
Mehdi Kaytoue
Jian Pei
  • Function : Author
  • PersonId : 906319

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.
Not file

Dates and versions

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

Identifiers

  • HAL Id : hal-00922255 , version 1

Cite

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⟩
302 View
0 Download

Share

Gmail Facebook Twitter LinkedIn More