Evolutionary algorithms and fuzzy sets for discovering temporal rules

• Autorzy: Stephen G. Matthews , Mario A. Gongora , Adrian A. Hopgood
• Języki publikacji: EN

Abstrakty

EN

A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method's ability in a controlled experiment. It is shown that the method is capable of discovering temporal patterns, and the effect of Boolean itemset support on the efficacy of discovering temporal fuzzy association rules is presented.

Słowa kluczowe

EN

fuzzy association rules; temporal association rules; multi-objective evolutionary algorithm

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2013
• Tom: 23
• Numer: 4
• Strony: 855-868

Daty

wydano

2013

otrzymano

2012-02-13

poprawiono

2013-04-25

poprawiono

2013-07-17

Twórcy

autor

Stephen G. Matthews

• Intelligent Systems Laboratory, Department of Engineering Mathematics, University of Bristol, Bristol, BS8 1UB, UK

autor

Mario A. Gongora

• Centre for Computational Intelligence, De Montfort University, The Gateway, Leicester, LE1 9BH, UK

autor

Adrian A. Hopgood

• Sheffield Business School, Sheffield Hallam University, Sheffield, S1 1WB, UK

Bibliografia

• Agrawal, R., Imieliński, T. and Swami, A. (1993). Mining association rules between sets of items in large databases, Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington, DC, USA, pp. 207-216.
• Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rules, Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, pp. 487-499.
• Agrawal, R. and Srikant, R. (1995). Mining sequential patterns, Proceedings of the 11th International Conference on Data Engineering, Taipei, Taiwan, pp. 3-14.
• Alcalá, R., Alcalá-Fdez, J., Gacto, M. and Herrera, F. (2007a). A multi-objective evolutionary algorithm for rule selection and tuning on fuzzy rule-based systems, Proceedings of the IEEE International Fuzzy Systems Conference (FUZZIEEE 2007), London, UK, pp. 1-6.
• Alcalá, R., Alcalá-Fdez, J. and Herrera, F. (2007b). A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection, IEEE Transactions on Fuzzy Systems 15(4): 616-635.
• Ale, J.M. and Rossi, G. H. (2000). An approach to discovering temporal association rules, Proceedings of the 2000 ACM Symposium on Applied Computing (SAC'00), Como, Italy, pp. 294-300.
• Bayardo, Jr., R.J. and Agrawal, R. (1999). Mining the most interesting rules, Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, pp. 145-154.
• Ben Aicha, F., Bouani, F. and Ksouri, M. (2013). A multivariable multiobjective predictive controller, International Journal of Applied Mathematics and Computer Science 23(1): 35-45, DOI: 10.2478/amcs-2013-0004.
• Carmona, C., González, P., del Jesus, M. and Herrera, F. (2010). NMEEF-SD: Non-dominated multiobjective evolutionary algorithm for extracting fuzzy rules in subgroup discovery, IEEE Transactions on Fuzzy Systems 18(5): 958-970.
• Casillas, J., Cordón, O., del Jesus, M. and Herrera, F. (2005). Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction, IEEE Transactions on Fuzzy Systems 13(1): 13-29.
• Chan, K.C.C. and Au, W.-H. (1997). Mining fuzzy association rules, Proceedings of the 6th International Conference on Information and Knowledge Management, Las Vegas, NV, USA, pp. 209-215.
• Cordón, O. and Herrera, F. (1997). Identification of linguistic fuzzy models by means of genetic algorithms, in D. Driankov and H. Hellendoorn (Eds.), Fuzzy Model Identification. Selected Approaches, Springer-Verlag, Heidelberg, pp. 215-250.
• Cordón, O., Herrera, F., Hoffmann, F. and Magdalena, L. (2001). Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases, Advances in Fuzzy Systems-Applications and Theory, World Scientific, Singapore.
• Deb, K. (2005). Multi-objective optimization, in E.K. Burke and G. Kendall (Eds.), Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, Springer, Berlin, pp. 403-449.
• Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation 6(2): 182-197.
• del Jesus, M.J., Gámez, J.A., González, P. and Puerta, J.M. (2011). On the discovery of association rules by means of evolutionary algorithms, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1(5): 397-415.
• Dubois, D. and Prade, H.M. (1980). Fuzzy Sets and Systems: Theory and Applications, Academic Press, London.
• Freitas, A.A. (2002). Data Mining and Knowledge Discovery with Evolutionary Algorithms, Springer-Verlag, Berlin/Heidelberg.
• Gacto, M., Alcalá, R. and Herrera, F. (2011). Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures, Information Sciences 181(20): 4340-4360.
• Ghosh, A. and Nath, B. (2004). Multi-objective rule mining using genetic algorithms, Information Sciences 163(1-3): 123-133.
• Han, J., Gong, W. and Yin, Y. (1998). Mining segment-wise periodic patterns in time-related databases, Proceedings of the 4th 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): 27-46.
• Homaifar, A. and McCormick, E. (1995). Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms, IEEE Transactions on Fuzzy Systems 3(2): 129-139.
• Hong, T.-P., Chen, C.-H., Lee, Y.-C. and Wu, Y.-L. (2008). Genetic-fuzzy data mining with divide-and-conquer strategy, IEEE Transactions on Evolutionary Computation 12(2): 252-265.
• Hong, T.-P., Kuo, C.-S. and Chi, S.-C. (2001). Trade-off between computation time and number of rules for fuzzy mining from quantitative data, International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems 9(5): 587-604.
• Hong, T.-P. and Lee, Y.-C. (2008). An overview of mining fuzzy association rules, in H. Bustince, F. Herrera and J. Montero (Eds.), Fuzzy Sets and Their Extensions: Representation, Aggregation and Models, Studies in Fuzziness and Soft Computing, Vol. 220, Springer, Berlin/Heidelberg, pp. 397-410.
• Hopgood, A. (2012). Intelligent Systems for Engineers and Scientists, 3rd edn, CRC Press, Boca Raton, FL.
• Kaya, M. (2009). MOGAMOD: Multi-objective genetic algorithm for motif discovery, Expert Systems with Applications 36(2, Part 1): 1039-1047.
• Kaya, M. and Alhajj, R. (2003). Facilitating fuzzy association rules mining by using multi-objective genetic algorithms for automated clustering, Proceedings of the 3rd IEEE International Conference on Data Mining, Melbourne, FL, USA, pp. 561-564.
• Klir, G.J., Clair, U.H.S. and Yuan, B. (1997). Fuzzy Set Theory: Foundations and Applications, Prentice Hall, Upper Saddle River, NJ.
• Kuok, C.M., Fu, A. and Wong, M.H. (1998). Mining fuzzy association rules in databases, SIGMOD Record 27(1): 41-46.
• Laxman, S. and Sastry, P. S. (2006). A survey of temporal data mining, Sādhanā 31(2): 173-198.
• Lee, W.-J. and Lee, S.-J. (2004). Discovery of fuzzy temporal association rules, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34(6): 111-118.
• Leonard, D. (2005). After Katrina: Crisis management, the only lifeline was the Wal-Mart, FORTUNE Magazine (October 3).
• Li, Y., Ning, P., Wang, X. S. and Jajodia, S. (2003). Discovering calendar-based temporal association rules, Data & Knowledge Engineering 44(2): 193-218.
• Lozano, M., Herrera, F., Krasnogor, N. and Molina, D. (2004). Real-coded memetic algorithms with crossover hill-climbing, Evolutionary Computation 12(3): 273-302.
• Maeda, A., Ashida, H., Taniguchi, Y. and Takahashi, Y. (1995). Data mining system using fuzzy rule induction, Proceedings of the 1995 IEEE International Conference on Fuzzy Systems, Yokohama, Japan, Vol. 5, pp. 45-46.
• Martínez-Ballesteros, M., Martínez-Álvarez, F., Troncoso, A. and Riquelme, J. (2011). An evolutionary algorithm to discover quantitative association rules in multidimensional time series, Soft Computing-A Fusion of Foundations, Methodologies and Applications 15(10): 1-20.
• Mata, J., Alvarez, J.L. and Riquelme, J.C. (2002). An evolutionary algorithm to discover numeric association rules, Proceedings of the 2002 ACM Symposium on Applied Computing, Madrid, Spain, pp. 590-594.
• Matthews, S.G., Gongora, M.A. and Hopgood, A.A. (2010). Evolving temporal association rules with genetic algorithms, in M. Bramer, M. Petridis and A. Hopgood (Eds.), Research and Development in Intelligent Systems XXVII, Springer, London, pp. 107-120.
• Matthews, S.G., Gongora, M.A. and Hopgood, A.A. (2011a). Evolving temporal fuzzy association rules from quantitative data with a multi-objective evolutionary algorithm, in E. Corchado, M. Kurzynski and M. Wozniak (Eds.), Hybrid Artificial Intelligent Systems (Proceedings of HAIS 2011), Lecture Notes in Computer Science, Vol. 6678, Springer, Berlin/Heidelberg, pp. 198-205.
• Matthews, S.G., Gongora, M.A. and Hopgood, A.A. (2011b). Evolving temporal fuzzy itemsets from quantitative data with a multi-objective evolutionary algorithm, IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS 2011), Paris, France, pp. 9-16.
• Miller, R.J. and Yang, Y. (1997). Association rules over interval data, ACM SIGMOD Record 26(2): 452-461.
• Mitsa, T. (2010). Temporal Data Mining, CRC Press Online, Boca Raton, FL.
• Özden, B., Ramaswamy, S. and Silberschatz, A. (1998). Cyclic association rules, Proceedings of the 1914 International Conference on Data Engineering, Orlando, FL, USA, pp. 412-421.
• Piatetsky-Shapiro, G. (1990). Knowledge discovery in real databases: A report on the IJCAI-89 workshop, AI Magazine 11(4): 68-70.
• Saleh, B. and Masseglia, F. (2010). Discovering frequent behaviors: Time is an essential element of the context, Knowledge and Information Systems 28(2): 1-21.
• Srikant, R. and Agrawal, R. (1996). Mining quantitative association rules in large relational tables, Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Quebec, Canada, pp. 1-12.
• Venturini, G. (1993). SIA: A supervised inductive algorithm with genetic search for learning attributes based concepts, Proceedings of the European Conference on Machine Learning (ECML-93), Vienna, Austria, pp. 280-296.
• Weng, C.-H. (2011). Mining fuzzy specific rare itemsets for education data, Knowledge-Based Systems 24(5): 697-708.
• Yoo, J.S. and Shekhar, S. (2009). Similarity-profiled temporal association mining, IEEE Transactions on Knowledge and Data Engineering 21(8): 1147-1161.
• Zadeh, L.A. (1965). Fuzzy sets, Information Control 8(3): 338-353.
• Zadeh, L.A. (1975). The concept of a linguistic variable and its application to approximate reasoning, Parts I, II, III, Information Sciences 8-9(3,4,1): 199-249, 301-357, 43-80.

Identyfikatory

DOI

10.2478/amcs-2013-0064