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Fuzzy Association Rule Mining with Type-2 Membership Functions

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Intelligent Information and Database Systems (ACIIDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9012))

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Abstract

In this paper, a fuzzy association rule mining approach with type-2 membership functions is proposed for dealing with data uncertainty. It first transfers quantitative values in transactions into type-2 fuzzy values. Then, according to a predefined split number of points, they are reduced to type-1 fuzzy values. At last, the fuzzy association rules are derived by using these fuzzy values. Experiments on a simulated dataset were made to show the effectiveness of the proposed approach.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Tamkang University, Taipei, 251, Taiwan

    Chun-Hao Chen

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

    Tzung-Pei Hong & Yu Li

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

    Tzung-Pei Hong

Authors
  1. Chun-Hao Chen
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  2. Tzung-Pei Hong
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  3. Yu Li
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Corresponding author

Correspondence to Tzung-Pei Hong .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh city, Vietnam

    Ngoc Thanh Nguyen

  2. Wroclaw University of Technology, Wroclaw, Poland

    Bogdan Trawiński

  3. Bina Nusantara University, Jakarta, Indonesia

    Raymond Kosala

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Chen, CH., Hong, TP., Li, Y. (2015). Fuzzy Association Rule Mining with Type-2 Membership Functions. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9012. Springer, Cham. https://doi.org/10.1007/978-3-319-15705-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-15705-4_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15704-7

  • Online ISBN: 978-3-319-15705-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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