Abstract
In recent years, association rules from large databases have received considerable attention and have been applied to various areas such as marketing, retail and finance, et al. While conventional approaches usually deal with databases with binary values, this chapter introduces an approach to discovering association rules from quantitative datasets. To remedy possible boundary problems due to sharp partitioning and to represent linguistic knowledge, fuzzy logic is used to “discretize” quantitative domains. A method of finding fuzzy sets for each quantitative attribute by using clustering is proposed based on different overlapping degrees. This proposed method is then applied to two real datasets housing and credit.
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Yan, P., Chen, G. (2006). Fuzzy Quantitative Association Rules and Its Applications. In: Kahraman, C. (eds) Fuzzy Applications in Industrial Engineering. Studies in Fuzziness and Soft Computing, vol 201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33517-X_23
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DOI: https://doi.org/10.1007/3-540-33517-X_23
Publisher Name: Springer, Berlin, Heidelberg
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