Evolving temporal association rules with genetic algorithms

Contribution for newspaper or weekly magazine, Part of book or chapter of book English OPEN
Matthews, Stephen G.; Gongora, Mario A.; Hopgood, Adrian A.;
  • Publisher: Springer
  • Related identifiers: doi: 10.1007/978-0-85729-130-1_8
  • Subject:
    acm: ComputingMethodologies_PATTERNRECOGNITION | InformationSystems_DATABASEMANAGEMENT

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 min... View more
  • References (22)
    22 references, page 1 of 3

    1. Agrawal, R., Imielin´ski, 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.

    2. 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.

    3. 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.

    4. 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.

    5. 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.

    6. 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.

    7. De Jong, K.A. (2006) Evolutionary computation: a unified approach. MIT Press, Cambridge, MA, USA.

    8. 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

    9. Freitas, A.A. (2002) Data mining and knowledge discovery with evolutionary algorithms. Springer-Verlag.

    10. Ghandar, A., Michalewicz, Z., Schmidt, M., Toˆ, T.-D. and Zurbrugg, R. (2009) Computational intelligence for evolving trading rules. IEEE Transactions on Evolutionary Computation, 13(1), pp. 71-86.

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