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• W2092974027 abstract "Formal Concept Analysis (FCA), in which data is represented as a formal context, offers a framework for Association Rules Mining (ARM) by handling functional dependencies in the data. However, with the size of the formal context, the number of rules grows exponentially. In this article, we apply Fuzzy K-Means clustering on the data set to reduce the formal context and FCA on the reduced data set for mining association rules. With experiments on two real-world healthcare data sets, we offer the evidence for performance of FKM-based FCA in mining association rules." @default.
• W2092974027 created "2016-06-24" @default.
• W2092974027 creator A5085408978 @default.
• W2092974027 date "2012-03-01" @default.
• W2092974027 modified "2023-09-24" @default.
• W2092974027 title "FUZZY CLUSTERING-BASED FORMAL CONCEPT ANALYSIS FOR ASSOCIATION RULES MINING" @default.
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• W2092974027 doi "https://doi.org/10.1080/08839514.2012.648457" @default.
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