Paper published in a book (Scientific congresses and symposiums)
Evolving Temporal Association Rules with Genetic Algorithms
Matthews, Stephen G.; Gongora, Mario A.; Hopgood, Adrian
2011 • In Bramer, Max; Petridis, Miltos; Hopgood, Adrian (Eds.) Research and Development in Intelligent Systems XXVII: Incorporating Applications and Innovations in Intelligent Systems XVIII Proceedings of AI-2010, The Thirtieth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
[en] A novel framework for mining temporal association rules by discovering
itemsets with a genetic algorithm is introduced. Metaheuristics have been applied
to association rule mining, we show the efficacy of extending this to another
variant - temporal association rule mining. Our framework is an enhancement to
existing temporal association rule mining methods as it employs a genetic algorithm
to simultaneously search the rule space and temporal space. A methodology for
validating the ability of the proposed framework isolates target temporal itemsets in
synthetic datasets. The Iterative Rule Learning method successfully discovers these
targets in datasets with varying levels of difficulty.
Disciplines :
Computer science
Author, co-author :
Matthews, Stephen G.
Gongora, Mario A.
Hopgood, Adrian ; Université de Liège > HEC - Ecole de gestion de l'ULG : Direction générale
Language :
English
Title :
Evolving Temporal Association Rules with Genetic Algorithms
Publication date :
2011
Event name :
AI-2010: 30th SGAI International Conference on Artificial Intelligence
Event place :
Cambridge, United Kingdom
Event date :
14-16 Dec. 2010
Main work title :
Research and Development in Intelligent Systems XXVII: Incorporating Applications and Innovations in Intelligent Systems XVIII Proceedings of AI-2010, The Thirtieth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
Editor :
Bramer, Max
Petridis, Miltos
Hopgood, Adrian ; Université de Liège - ULiège > HEC Liège : UER > UER Opérations
Agrawal, R., Imieliński, T. and Swami, A. (1993) Mining association rules between sets of items in large databases. In: Proceedings of ACM SIGMOD international conference on Management of data, Washington, DC, USA, pp. 206-217.
Agrawal, R. and Srikant, R. (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, pp. 487-499.
Alcala-Fdez, J., Flugy-Pape, N., Bonarini, A. and Herrera, F. (2010) Analysis of the Effectiveness of the Genetic Algorithms based on Extraction of Association Rules. Fundamenta Informaticae, 98(1), pp. 1-14.
Ale, J. and Rossi, G. (2000) An approach to discovering temporal association rules. In: Proceedings of the 2000 ACM Symposium on Applied computing (SAC 00) New York, NY, USA, pp. 294-300.
Au, W. and Chan, K. (2002) An evolutionary approach for discovering changing patterns in historical data. In: Proceedings Of The Society Of Photo-Optical Instrumentation Engineers (SPIE), Orlando, FL, USA, pp. 398-409.
Chang, C-Y., Chen, M-S. and Lee, C-H. (2002) Mining general temporal association rules for items with different exhibition periods. In: Proceedings of the 2002 IEEE International Conference on Data Mining, Maebashi City, Japan, pp. 59-66.
De Jong, K.A. (2006) Evolutionary computation: A unified approach. MIT Press, Cambridge, MA, USA.
Dong, G. and Li, J. (1999) Efficient mining of emerging patterns: Discovering trends and differences. In: Proceedings of the fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 43-52.
Freitas, A.A. (2002) Data mining and knowledge discovery with evolutionary algorithms. Springer-Verlag.
Ghandar, A., Michalewicz, Z., Schmidt, M., T̂o, T.-D. and Zurbrugg, R. (2009 Computational intelligence for evolving trading rules. IEEE Transactions on Evolutionary Computation, 13(1), pp. 71-86.
Giannella, C. (2003) IBM Quest Market-Basket Synthetic Data Generator. http://www.cs.nmsu.edu/ cgiannel/assoc gen.html. Cited 29 May 2009.
Han, J., Gong, W. and Yin, Y. (1998) Mining segment-wise periodic patterns in time-related databases. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, NY, USA. pp. 214-218.
Herrera, F. (2008) Genetic fuzzy systems: Taxonomy, current research trends and prospects. Evolutionary Intelligence, 1(1), pp. 27-46.
Holland, J.H. (1975) Adaptation in natural and articial systems. University of Michigan Press, Ann Arbor.
Huang, J-W., Dai, B-R. and Chen, M-S. (2007) Twain: Two-end association miner with precise frequent exhibition periods. ACM Transactions on Knowledge Discovery from Data, 1(2), Article 8.
Kuo, R., Chao, C. and Chiu, Y. (2009) Application of particle swarm optimization to association rule mining. Applied Soft Computing, In Press, Corrected Proof.
Laxman, S. and Sastry, P.S. (2006) A survey of temporal data mining. S̄adhan̄a, 31, pp. 173-198.
Li, Y., Ning, P., Wang, X. S. and Jajodia, S. (2003) Discovering calendar-based temporal association rules. Data & Knowledge Engineering, 44(2), pp. 193-218.
Mata, J., Alvarez, J. L. and Riquelme, J. C. (2002) An evolutionary algorithm to discover numeric association rules. In: Proceedings of the 2002 ACM Symposium on Applied Computing, New York, NY, USA, pp. 590-594.
Ö zden, B., Ramaswamy, S. and Silberschatz, A. (1998) Cyclic Association Rules. In: Proceedings of the Fourteenth International Conference on Data Engineering, Washington, DC, USA, pp. 412-421.
Tan, P-N., Steinbach, M. and Kumar, V. (2005) Introduction to Data Mining, AddisonWesley, Boston, MA, USA.
Yan, X., Zhang, C. and Zhang, S. (2009) Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. Expert Systems with Applications, 36(2), pp. 3066-3076.
