Fuzzy Association Rule Mining with Type-2 Membership Functions

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• 作者：Chun-Hao Chen (7)
  Tzung-Pei Hong (8) (9)
  Yu Li (8)
  
  7. Department of Computer Science and Information Engineering ; Tamkang University ; Taipei ; 251 ; Taiwan
  8. Department of Computer Science and Engineering ; National Sun Yat-sen University ; Kaohsiung ; Taiwan
  9. Department of Computer Science and Information Engineering ; National University of Kaohsiung ; Kaohsiung ; Taiwan
  
• 关键词：Data mining ; Fuzzy association rule ; Membership functions ; Type ; 2 fuzzy set
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9012
• 期：1
• 页码：128-134
• 全文大小：1,249 KB
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  14. Lee, Y-C, Hong, T-P, Lin, W-Y Mining Fuzzy Association Rules with Multiple Minimum Supports Using Maximum Constraints. In: Negoita, MG, Howlett, RJ, Jain, LC eds. (2004) Knowledge-Based Intelligent Information and Engineering Systems. Springer, Heidelberg, pp. 1283-1290 CrossRef
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  19. Pei, B, Zhao, S, Chen, H, Zhou, X, Chen, D (2013) FARP: Mining fuzzy association rules from a probabilistic quantitative database. Information Sciences 237: pp. 242-260 CrossRef
  20. Sowan, B, Dahal, K, Hossain, MA, Zhang, L, Spencer, L (2013) Fuzzy association rule mining approaches for enhancing prediction performance. Expert Systems with Applications 40: pp. 6928-6937 CrossRef
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• 作者单位：Intelligent Information and Database Systems
• 丛书名：978-3-319-15704-7
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

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.