Title :
Mining Statistically Significant Sequential Patterns
Author :
Low-Kam, Cecile ; Raissi, Chedy ; Kaytoue, Mehdi ; Jian Pei
Author_Institution :
Montreal Heart Inst., Univ. de Montreal, Montreal, QC, Canada
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 item sets. 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.
Keywords :
computer games; data mining; statistical analysis; frequent pattern mining framework; game strategies; interest pattern extraction; item set sequences; multiple testing procedure; pattern discovery; sequences null model; statistically significant sequential pattern mining; video game; Computational modeling; Data mining; Entropy; Hidden Markov models; Itemsets; Random variables; Sequential pattern; null model; significance test;
Conference_Titel :
Data Mining (ICDM), 2013 IEEE 13th International Conference on
Conference_Location :
Dallas, TX
DOI :
10.1109/ICDM.2013.124
