A Fuzzy Close Algorithm for Mining Fuzzy Association Rules

被引：5

作者：

Pierrard, Regis ^{[1
,2
]}

Poli, Jean-Philippe ^{[1
]}

Hudelot, Celine ^{[2
]}

机构：

[1] CEA, LIST, Data Anal & Syst Intelligence Lab, F-91191 Gif Sur Yvette, France

[2] Paris Saclay Univ, CentraleSupelec, Math Interacting Comp Sci, F-91190 Gif Sur Yvette, France

来源：

INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS: THEORY AND FOUNDATIONS, PT II | 2018年 / 854卷

关键词：

Fuzzy data mining; Fuzzy closure operator; Frequent itemsets mining; Fuzzy logic; Association rules;

D O I：

10.1007/978-3-319-91476-3_8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Association rules allow to mine large datasets to automatically discover relations between variables. In order to take into account both qualitative and quantitative variables, fuzzy logic has been applied and many association rule extraction algorithms have been fuzzified. In this paper, we propose a fuzzy adaptation of the well-known Close algorithm which relies on the closure of itemsets. The Close-algorithm needs less passes over the dataset and is suitable when variables are correlated. The algorithm is then compared to other on public datasets.

引用

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页码：88 / 99

页数：12

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