Harbin Institute of Technology (Shenzhen)
• Algorithms for sequence analysis, sequence prediction and pattern mining (2010...) • Data Mining Algorithms with Applications in Tutoring Agents (2008  2011) • Evaluating Spatial Reasoning in Intelligent Tutoring Systems (2006  2010) • A Cognitive and OntologyBased Model for Building GlassBox Learning Objects (20052008) • A Cognitive Model for Building "Cognitive Tutors"(20052010) 
Algorithms to discover sequential patterns A sequence is an ordered list of symbols. A sequence is a general concept that exists in many domains. For example, in the domain of bioinformatics, protein sequences, microarray data and DNA fragments are sequences. Examples from other domains are sequences of webpages visited by users, sequences of transactions made by customers in a store, sequences of weather observations, educational data or medical record data. To analyse sequences, a popular technique is to apply algorithms to discover sequential patterns such as PrefixSpan, SPAM, Spade and AprioriALL. I have developped multiple algorithms for discovering sequential patterns in sequences such as:
These algorithms have been applied in various domains such as elearning (in the CanadarmTutor project), for webclick stream analysis (FournierViger et al., 2012), manufacturing simulation (KamsuFoguem et al., 2013), quality control (Bogon et al., 2012), analyzing visitor movements (Orellana, 2011), modelling trends on social web (Christiansen, 2013) and restaurant recommendation (Han et al., 2012). Source code of the algorithms have been released as part of the opensource data mining library SPMF. Algorithms to discover association rules and itemsets I have also developped or collaborated on several algorithms for discovering itemsets and associations in transaction databases such as:
Algorithms for sequence prediction I have also made contribution on the topic of sequence prediction.

Main publications
[1] Gueniche, T., FournierViger, P., Tseng, V.S. (2013). Compact Prediction Tree: A Lossless Model for Accurate Sequence Prediction. Proc. 9th International Conference on Advanced Data Mining and Applications (ADMA 2013), Springer, LNAI, 12 pages (to appear).  
[2] FournierViger, P., Wu, C.W., Tseng, V.S. (2013). Mining Maximal Sequential Patterns without Candidate Maintenance. Proc. 9th International Conference on Advanced Data Mining and Applications (ADMA 2013), Springer, LNAI, 12 pages (to appear).  
[3] FournierViger, P., Gomariz, A., Gueniche, T., Mwamikazi, E., Thomas, R. (2013). Efficient Mining of TopK Sequential Patterns. Proc. 9th International Conference on Advanced Data Mining and Applications (ADMA 2013), Springer, LNAI, 12 pages (to appear).  
[4] FournierViger, P., Mwamikazi, E., Gueniche, T., Faghihi, U. (2013). Memory Efficient Itemset Tree for Targeted Association Rule Mining. Proc. 9th International Conference on Advanced Data Mining and Applications (ADMA 2013), Springer, LNAI, 12 pages (to appear).  
[5] FournierViger, P., Tseng, V. S. (2013). TNS: Mining TopK NonRedundant Sequential Rules. Proc. 28th Symposium on Applied Computing (ACM SAC 2013). ACM Press, pp. 164166.  
[6] FournierViger, P., Tseng, V.S. (2012). Mining TopK NonRedundant Association Rules. Proc. 20th International Symposium on Methodologies for Intelligent Systems (ISMIS 2012), Springer, LNCS 7661, pp. 31 40.  
[7] FournierViger, P. Gueniche, T., Tseng, V.S. (2012). Using PartiallyOrdered Sequential Rules to Generate More Accurate Sequence Prediction. Proc. 8th International Conference on Advanced Data Mining and Applications (ADMA 2012), Springer LNAI 7713, pp.431442.  
[8] FournierViger, P., Faghihi, U., Nkambou, R., Mephu Nguifo, E. (2012). CMRules: Mining Sequential Rules Common to Several Sequences. Knowledgebased Systems, Elsevier, 25(1): 6376.  
[9] FournierViger, P., Wu, C.W., Tseng, V.S., Nkambou, R. (2012). Mining Sequential Rules Common to Several Sequences with the Window Size Constraint. Proceedings of the 25th Canadian Conf. on Artificial Intelligence (AI 2012), Springer, LNAI 7310, pp.299304.  
[10]Wu, C.W., FournierViger, P., Yu., P. S., Tseng, V. S. (2011). Efficient Mining of a Concise and Lossless Representation of High Utility Itemsets. Proceedings of the 11th IEEE International Conference on Data Mining (ICDM 2011). IEEE CS Press, pp.824833.  
[11] FournierViger, P. & Tseng, V. S. (2011). Mining TopK Sequential Rules. Proceedings of the 7th Intern. Conf. on Advanced Data Mining and Applications (ADMA 2011). LNAI 7121, Springer, pp.180194.  
[12] FournierViger, P., Nkambou, R. & Tseng, V. S. (2011). RuleGrowth: Mining Sequential Rules Common to Several Sequences by PatternGrowth. Proceedings of the 26th Symposium on Applied Computing (ACM SAC 2011). ACM Press, pp. 954959.  
(see the "publications" section of the webiste for more articles) 