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Hybridization of GRASP Metaheuristic with Data Mining Techniques

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Journal of Mathematical Modelling and Algorithms

Abstract

In this work, we propose a hybridization of GRASP metaheuristic that incorporates a data mining process. We believe that patterns obtained from a set of sub-optimal solutions, by using data mining techniques, can be used to guide the search for better solutions in metaheuristics procedures. In this hybrid GRASP proposal, after executing a significant number of GRASP iterations, the data mining process extracts patterns from an elite set of solutions which will guide the following iterations. To validate this proposal we have worked on the Set Packing Problem as a case study. Computational experiments, comparing traditional GRASP and different hybrid approaches, show that employing frequent patterns mined from an elite set of solutions conducted to better results. Besides, additional performed experiments evidence that data mining strategies accelerate the process of finding good solutions.

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References

  1. Agrawal, R., Imielinski, T. and Srikant, R.: Mining association rules between sets of items in large databases, in ACM SIGMOD Intl. Conf. on Management of Data, 1993, pp. 207–216.

  2. Agrawal, R. and Srikant, R.: Fast algorithms for mining association rules, in 20th Very Large DataBase Conference, 1994, pp. 487–499.

  3. Bastide, Y., Taouil, R., Pasquier, N., Stumme, G. and Lakhal, L.: Mining frequent patterns with counting inference, ACM SIGKDD Explorations Newsletter 2 (2000), 66–75.

  4. Berry, M. L. A. and Linoff, G.: Data Mining Techniques: for Marketing, Sales and Customer Support, Wiley Computer, 1997.

  5. Delorme, X., Gandibleux, X. and Rodriguez, J.: GRASP for set packing problems, Eur. J. Oper. Res. 153 (2003), 564–580.

    Article  MathSciNet  Google Scholar 

  6. Feo, T. A. and Resende, M. G. C.: Greedy randomized adaptive search procedures, J. Glob. Optim. 6 (1995), 109–133.

    Article  MATH  MathSciNet  Google Scholar 

  7. Gandibleux, X., Delorme, X. and T’Kindt, V.: An ant colony algorithm for the set packing problem, Note de Recherche, no 13, LAMIH-ROI, Université de Valeciennes et du Hainaut-Cambrésis, 2004.

  8. Garey, M. R. and Johnson, D. S.: Computers and Intractability: A Guide to the Theory of NP-Completeness, V. H. Freeman and Company, 1979.

  9. Guo, Y., Lim, A., Rodrigues, B. and Zhu, Y.: Heuristics for a brokering set packing problem, in 8th Intl. Symposium on Artificial Intelligence and Mathematics, 2004.

  10. Goethals, B. and Zaki, M. J.: Advances in frequent itemset mining implementations: introduction to FIMI’03, in IEEE ICDM Workshop on Frequent Itemset Mining Implementations, 2003.

  11. Grahne, G. and Zhu, J.: Efficiently using prefix-trees in mining frequent itemsets, in IEEE ICDM Workshop on Frequent Itemset Mining Implementations, 2003.

  12. Han, J. and Kamber, M.: Data Mining: Concepts and Techniques, Morgan Kaufmann, 2000.

  13. Han, J., Pei, J. and Yin, Y.: Mining frequent patterns without candidate generation, in ACM SIGMOD Intl. Conf. on Management of Data, 2000, pp. 1–12.

  14. Lodi, A., Allemand, K. and Liebling, T. M.: An evolutionary heuristic for quadratic 0-1 programming, Eur. J. Oper. Res. 119 (1999), 662–670.

    Article  MATH  Google Scholar 

  15. Mingozzi, A., Maniezzo, V., Ricciardelli, S. and Bianco, L.: An exact algorithm for the project scheduling with resource constraints based on a new mathematical formulation, Manag. Sci. 44 (1998), 714–729.

    Article  MATH  Google Scholar 

  16. Orlando, S., Palmerimi, P. and Perego, R.: Adaptive and resource-aware mining of frequent sets, in IEEE Intl. Conf. on Data Mining, 2002, pp. 338–345.

  17. Orlando, S., Palmerimi, P., Perego, R., Lucchese, C. and Silvestri, F.: kDCI++: A multi–strategy algorithm for discovering frequent sets in large databases, in IEEE ICDM Workshop on Frequent Itemset Mining Implementations, 2003.

  18. Padberg, M. W.: On the facial structure of set packing polyhedra, Math. Program. 5 (1973), 199–215.

    Article  MATH  MathSciNet  Google Scholar 

  19. Pardalos, P. M. and Rodgers, G. P.: Computational aspects of a branch and bound algorithm for quadratic zero-one programming, Computing 45 (1990), 131–144.

    Article  MATH  MathSciNet  Google Scholar 

  20. Park, J. S., Chen, M. and Yu, P. S.: An effective hash-based algorithm for mining association rules, in ACM SIGMOD Intl. Conf. on Management of Data, 1995, pp. 175–186.

  21. Savasere, A., Omiecinski, E. and Navathe, S.: An efficient algorithm for mining association rules in large databases, in 21st Very Large DataBase Conference, 1995, pp. 432–444.

  22. Talbi, E. G.: A taxonomy of hybrid metaheuristics, Journal of Heuristics 8 (2002), 541–564.

    Article  Google Scholar 

  23. Zwaneveld, P. J., Kroon, L. G., Romeijin, H. E., Salomon, M., Dauzères-Pérès, S., Van Hoesel, S. P. and Ambergen, H. W.: Routing trains through railway stations: model formulation and algorithms, Trans. Sci. 30 (1996), 181–194.

    MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Universidade Federal Fluminense, Rua Passo da Pátria, 156 – Bloco E – 3° andar – Boa Viagem, 24210-240, Niterói, RJ, Brazil

    Marcos Henrique Ribeiro, Alexandre Plastino & Simone L. Martins

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  1. Marcos Henrique Ribeiro
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  2. Alexandre Plastino
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  3. Simone L. Martins
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Corresponding author

Correspondence to Marcos Henrique Ribeiro.

Additional information

★★Work sponsored by CNPq research grants 300879/00-8 and 475124/03-0.

†Work sponsored by CNPq research grant 475124/03-0.

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Ribeiro, M.H., Plastino, A. & Martins, S.L. Hybridization of GRASP Metaheuristic with Data Mining Techniques. J Math Model Algor 5, 23–41 (2006). https://doi.org/10.1007/s10852-005-9030-1

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  • DOI: https://doi.org/10.1007/s10852-005-9030-1

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