Mining Fuzzy Association Rules in the Framework of AFS Theory

被引：0
作者：
Wang B. [1 ]
Liu X.-D. [1 ]
Wang L.-D. [1 ]
机构：
[1] Department of Mathematics, Dalian Maritime University, Dalian
来源：
Ann. Data Sci. | / 3卷 / 261-270期
基金：
中国国家自然科学基金;
关键词：
AFS fuzzy logic; Degrees of implication; Fuzzy association rules; Molecular lattices;
D O I：
10.1007/s40745-015-0059-3
中图分类号：
学科分类号：
摘要：
In this paper, firstly we study the representations and fuzzy logic operations for the fuzzy concepts in real data systems. Secondly, we propose a new fuzzy association rule mining algorithm in the framework of AFS (Axiomatic Fuzzy Sets) theory. Compared with the current algorithms, the advantage of proposed algorithm has two advantages. One is that the membership functions of the fuzzy sets representing the extracted rules and the fuzzy logic operations applied to extract fuzzy rules are determined by the distribution of the data, instead of the fuzzy sets defined by some special functions, t-norm, t-conorm, negation operator, implication operator and fuzzy similarity relation given in advance. The extracted fuzzy rules are interpretable and similar to human intuition. Another is that its simplicity in implementation and mathematical beauty in fuzzy theory, and can be directly applied to extract fuzzy association rules in real data systems. Finally, a well-known example Iris dataset is used to illustrate the effectiveness of the new algorithm based on the proposed degrees of implication. We obtained reclassification accuracy 98 %. © 2015, Springer-Verlag Berlin Heidelberg.
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页码：261 / 270
页数：9
相关论文
共 25 条
  • [1] Luger G.F., Artificial Intelligence, (2005)
  • [2] Anderson T.W., An introduction to multivariate statistical analysis, (1984)
  • [3] Arnold S.F., Mathematical statistics, (1990)
  • [4] Agrawal R., Imielinski T., Swami A., Mining association rules between sets of items in large databases, In: Proceeding of the ACM SIGMOD Conference on Management of Data, Washington, DC, pp. 207-216, (1993)
  • [5] Han J., Kamber M., Data mining: concepts and techniques, (2000)
  • [6] Srikant R., Agrawal R., Mining quantitative association rules in large relational tables, SIGMOD 96 6/96 Montreal, (1996)
  • [7] Yan P., Chen G.Q., Discovering a cover set of ARsi with hierarchy from quantitative databases, Inf Sci, 173, (2005)
  • [8] Chen G.Q., Wei Q., Fuzzy association rules and the extended mining algorithms, Inf Sci, 147, pp. 201-228, (2002)
  • [9] Delgado M., Marin N., Sachez D., Vila M.A., Fuzzy association rules: general model and applications, IEEE Trans Fuzzy Syst, 11, 2, pp. 214-225, (2003)
  • [10] Berzal F., Blanco I., Sanchez D., Serrano J.M., Vila M.A., A definition for fuzzy approximate dependencies, Fuzzy Sets Syst, 149, pp. 105-129, (2005)
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