Mining Statistically Significant Sequential Patterns

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 bas...

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Bibliographic Details
Published in2013 IEEE 13th International Conference on Data Mining pp. 488 - 497
Main Authors Low-Kam, Cecile, Raissi, Chedy, Kaytoue, Mehdi, Jian Pei
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.12.2013
Subjects
Computational modeling
Conferences
Data mining
Electronic & video games
Entropy
Games
Hidden Markov models
Itemsets
Methodology
Mines
null model
Pattern analysis
Random variables
Sequential pattern
significance test
Strategy
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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
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content type line 23
SourceType-Conference Papers & Proceedings-2
ISSN:1550-4786
2374-8486
DOI:10.1109/ICDM.2013.124