Memetic Computing

, Volume 10, Issue 1, pp 15–28 | Cite as

Mining fuzzy association rules using a memetic algorithm based on structure representation

  • Chuan-Kang Ting
  • Rung-Tzuo Liaw
  • Ting-Chen Wang
  • Tzung-Pei Hong
Regular Research Paper
  • 127 Downloads

Abstract

The association rules render the relationship among items and have become an important target of data mining. The fuzzy association rules introduce fuzzy set theory to deal with the quantity of items in the association rules. The membership functions play a key role in the fuzzification process and, therefore, significantly affect the results of fuzzy association rule mining. This study proposes a memetic algorithm (MA) for optimizing the membership functions in fuzzy association rule mining. The MA adopts a novel chromosome representation that considers the structures of membership functions. Based on the structure representation, we develop a local search operator to improve the efficiency of the MA in exploring good membership functions. Two local search strategies for the MA are further investigated. This study conducts a series of experiments to examine the proposed MA on different amounts of transactions. The experimental results show that the MA outperforms state-of-the-art evolutionary algorithms in terms of solution quality and convergence speed. These preferable results show the advantages of the structure-based representation and the local search in improving the performance. They also validate the high capability of the proposed MA in mining fuzzy association rules.

Keywords

Memetic computing Genetic algorithm Structure representation Local search Fuzzy association rules Membership function 

Notes

Acknowledgements

The authors would like to thank the editor and reviewers for their valuable comments and suggestions. This work was supported by the Ministry of Science and Technology of Taiwan, under contract MOST 104-2221-E-194-015-MY3.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the international conference on very large data bases, pp 487–499Google Scholar
  2. 2.
    Alcalá-Fdez J, Alcalá R, Herrera F (2011) A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning. IEEE Trans Fuzzy Syst 19(5):857–872CrossRefGoogle Scholar
  3. 3.
    Antonelli M, Ducange P, Marcelloni F (2014) A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers. Inf Sci 283(1):36–54MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Balázs K, Kóczy LT (2012) Genetic and bacterial memetic programming approaches in hierarchical-interpolative fuzzy system construction. In: Proceedings of IEEE international conference on fuzzy systems, pp 1–8Google Scholar
  5. 5.
    Cai G-R, Li S-Z, Chen S-L (2010) Mining fuzzy association rules by using nonlinear particle swarm optimization. Quant Log Soft Comput 82:621–630Google Scholar
  6. 6.
    Chan K, Au WH (1997) Mining fuzzy association rules. In: Proceedings of the international conference on information and knowledge management, pp 209–215Google Scholar
  7. 7.
    Chen C-H, Li A-F, Lee Y-C (2013) A fuzzy coherent rule mining algorithm. Appl Soft Comput 13(7):3422–3428CrossRefGoogle Scholar
  8. 8.
    Chen C-H, Tseng V-S, Hong T-P (2008) Cluster-based evaluation in fuzzy-genetic data mining. IEEE Trans Fuzzy Syst 16(1):249–262CrossRefGoogle Scholar
  9. 9.
    Chen M-S, Han J, Yu PS (1996) Data mining: an overview from a database perspective. IEEE Trans Knowl Data Eng 8(6):866–883CrossRefGoogle Scholar
  10. 10.
    El Majdouli MA, Rbouh I, Bougrine S, El Benani B, El Imrani AA (2016) Fireworks algorithm framework for big data optimization. Memet Comput 8(4):333–347Google Scholar
  11. 11.
    Fayyad UM, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag. 17:37–54Google Scholar
  12. 12.
    Fazzolari M, Alcala R, Nojima Y, Ishibuchi H, Herrera F (2013) A review of the application of multiobjective evolutionary fuzzy systems: current status and further directions. IEEE Trans Fuzzy Syst 21(1):45–65CrossRefGoogle Scholar
  13. 13.
    Feng L, Ong YS, Tan AH, Tsang IW (2015) Memes as building blocks: a case study on evolutionary optimization + transfer learning for routing problems. Memet Comput 7(3):159–180CrossRefGoogle Scholar
  14. 14.
    Gál L, Botzheim J, Kóczy LT, Ruano AE (2008) Fuzzy rule base extraction by the improved bacterial memetic algorithm. In: Proceedings of international symposium on applied machine intelligence and informatics, pp 49–53Google Scholar
  15. 15.
    Ho D-T, Garibaldi JM (2013) An improved optimisation framework for fuzzy time-series prediction. In: Proceedings of IEEE international conference on fuzzy systems, pp 1–8Google Scholar
  16. 16.
    Hong T-P, Chen C-H, Lee Y-C, Wu Y-L (2008) Genetic-fuzzy data mining with divide-and-conquer strategy. IEEE Trans Evol Comput 12(2):252–265CrossRefGoogle Scholar
  17. 17.
    Hong T-P, Kuo C-S, Chi S-C (1999) Mining association rules from quantitative data. Intell Data Anal 3(5):363–376CrossRefMATHGoogle Scholar
  18. 18.
    Hong T-P, Lee C-Y (1996) Induction of fuzzy rules and membership functions from training examples. Fuzzy Sets Syst 84(1):33–47MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Ji X-P, Cao X-B, Tang K (2016) Sequence searching and evaluation: a unified approach for aircraft arrival sequencing and scheduling problems. Memet Comput 8(2):109–123CrossRefGoogle Scholar
  20. 20.
    Kuok CM, Fu A, Wong MH (1998) Mining fuzzy association rules in databases. ACM SIGMOD Rec 27(1):41–46Google Scholar
  21. 21.
    Lee CK-H, Choy K-L, Ho GT-S, Lam CH-Y (2016) A slippery genetic algorithm-based process mining system for achieving better quality assurance in the garment industry. Expert Syst Appl 46:236–248CrossRefGoogle Scholar
  22. 22.
    Meng D, Pei Z (2012) Extracting linguistic rules from data sets using fuzzy logic and genetic algorithms. Neurocomputing 78(1):45–54CrossRefGoogle Scholar
  23. 23.
    Minaei-Bidgoli B, Barmaki R, Nasiri M (2013) Mining numerical association rules via multi-objective genetic algorithms. Inf Sci 233(1):15–24CrossRefGoogle Scholar
  24. 24.
    Mishra S, Mishra D, Satapathy SK (2011) Particle swarm optimization based fuzzy frequent pattern mining from gene expression data. In: Proceedings of the international conference on computer & communication technology, pp 15–20Google Scholar
  25. 25.
    Nekkaa M, Boughaci D (2015) A memetic algorithm with support vector machine for feature selection and classification. Memet Comput 7(1):59–73CrossRefGoogle Scholar
  26. 26.
    Ong YS, Lim MH, Chen XS (2010) Research frontier: memetic computation—past, present & future. IEEE Comput Intell Mag 5(2):24–36CrossRefGoogle Scholar
  27. 27.
    Prakash J, Singh PK (2015) An effective multiobjective approach for hard partitional clustering. Memet Comput 7(2):93–104CrossRefGoogle Scholar
  28. 28.
    Qodmanan HR, Nasiri M, Minaei-Bidgoli B (2011) Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence. Expert Syst Appl 38(1):288–298CrossRefGoogle Scholar
  29. 29.
    Rudzi\(\acute{\rm n}\)ski F (2016) A multi-objective genetic optimization of interpretability-oriented fuzzy rule-based classifiers. Appl Soft Comput 38:118–133Google Scholar
  30. 30.
    Samma H, Lim CP, Saleh JM, Suandi SA (2016) A memetic-based fuzzy support vector machine model and its application to license plate recognition. Memet Comput 8(3):235–251CrossRefGoogle Scholar
  31. 31.
    Srikant R, Agrawal R (1996) Mining quantitative association rules in large relational tables. ACM SIGMOD Rec 25(2):1–12CrossRefGoogle Scholar
  32. 32.
    Tsakonas A (2013) Local and global optimization for Takagi–Sugeno fuzzy system by memetic genetic programming. Expert Syst Appl 40(8):3282–3298CrossRefGoogle Scholar
  33. 33.
    Zhang Y, Liu J, Zhou MX, Jiang ZZ (2016) A multi-objective memetic algorithm based on decomposition for big optimization problems. Memet Comput 8(1):45–61CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational Chung Cheng UniversityChiayiTaiwan
  2. 2.Department of Computer Science and Information EngineeringNational University of KaohsiungKaohsiungTaiwan
  3. 3.Department of Computer Science and EngineeringNational Sun Yat-sen UniversityKaohsiungTaiwan

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