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

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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.

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Notes

  1. This study uses triangular membership functions; however, other shapes such as trapezoidal and bell functions are also applicable.

References

  1. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the international conference on very large data bases, pp 487–499

  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):857–872

    Article  Google Scholar 

  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):36–54

    Article  MathSciNet  MATH  Google Scholar 

  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 1–8

  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 82:621–630

    Google Scholar 

  6. Chan K, Au WH (1997) Mining fuzzy association rules. In: Proceedings of the international conference on information and knowledge management, pp 209–215

  7. Chen C-H, Li A-F, Lee Y-C (2013) A fuzzy coherent rule mining algorithm. Appl Soft Comput 13(7):3422–3428

    Article  Google Scholar 

  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):249–262

    Article  Google Scholar 

  9. Chen M-S, Han J, Yu PS (1996) Data mining: an overview from a database perspective. IEEE Trans Knowl Data Eng 8(6):866–883

    Article  Google Scholar 

  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):333–347

  11. Fayyad UM, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag. 17:37–54

    Google Scholar 

  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):45–65

    Article  Google Scholar 

  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):159–180

    Article  Google Scholar 

  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 49–53

  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 1–8

  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):252–265

    Article  Google Scholar 

  17. Hong T-P, Kuo C-S, Chi S-C (1999) Mining association rules from quantitative data. Intell Data Anal 3(5):363–376

    Article  MATH  Google Scholar 

  18. Hong T-P, Lee C-Y (1996) Induction of fuzzy rules and membership functions from training examples. Fuzzy Sets Syst 84(1):33–47

    Article  MathSciNet  MATH  Google Scholar 

  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):109–123

    Article  Google Scholar 

  20. Kuok CM, Fu A, Wong MH (1998) Mining fuzzy association rules in databases. ACM SIGMOD Rec 27(1):41–46

  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 46:236–248

    Article  Google Scholar 

  22. Meng D, Pei Z (2012) Extracting linguistic rules from data sets using fuzzy logic and genetic algorithms. Neurocomputing 78(1):45–54

    Article  Google Scholar 

  23. Minaei-Bidgoli B, Barmaki R, Nasiri M (2013) Mining numerical association rules via multi-objective genetic algorithms. Inf Sci 233(1):15–24

    Article  Google Scholar 

  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 15–20

  25. Nekkaa M, Boughaci D (2015) A memetic algorithm with support vector machine for feature selection and classification. Memet Comput 7(1):59–73

    Article  Google Scholar 

  26. Ong YS, Lim MH, Chen XS (2010) Research frontier: memetic computation—past, present & future. IEEE Comput Intell Mag 5(2):24–36

    Article  Google Scholar 

  27. Prakash J, Singh PK (2015) An effective multiobjective approach for hard partitional clustering. Memet Comput 7(2):93–104

    Article  Google Scholar 

  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):288–298

    Article  Google Scholar 

  29. Rudzi\(\acute{\rm n}\)ski F (2016) A multi-objective genetic optimization of interpretability-oriented fuzzy rule-based classifiers. Appl Soft Comput 38:118–133

  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):235–251

    Article  Google Scholar 

  31. Srikant R, Agrawal R (1996) Mining quantitative association rules in large relational tables. ACM SIGMOD Rec 25(2):1–12

    Article  Google Scholar 

  32. Tsakonas A (2013) Local and global optimization for Takagi–Sugeno fuzzy system by memetic genetic programming. Expert Syst Appl 40(8):3282–3298

    Article  Google Scholar 

  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):45–61

    Article  Google Scholar 

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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.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan

    Chuan-Kang Ting, Rung-Tzuo Liaw & Ting-Chen Wang

  2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  3. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan

    Tzung-Pei Hong

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  1. Chuan-Kang Ting
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  2. Rung-Tzuo Liaw
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  3. Ting-Chen Wang
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  4. Tzung-Pei Hong
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Correspondence to Chuan-Kang Ting.

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Ting, CK., Liaw, RT., Wang, TC. et al. Mining fuzzy association rules using a memetic algorithm based on structure representation. Memetic Comp. 10, 15–28 (2018). https://doi.org/10.1007/s12293-016-0220-3

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