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


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.


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



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.


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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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