Mining Emerging Gradual Patterns

被引：0

作者：

Laurent, Anne ^{[1
]}

Lesot, Marie-Jeanne ^{[2
,3
]}

Rifqi, Maria ^{[4
]}

机构：

[1] Univ Montpellier 2, LIRMM, Montpellier, France

[2] Univ Paris 06, Sorbonne Univ, UMR 7606, LIP6, F-75005 Paris, France

[3] CNRS, UMR 7606, LIP6, F-75005 Paris, France

[4] Univ Pantheon Assas, LEMMA, Paris, France

来源：

PROCEEDINGS OF THE 2015 CONFERENCE OF THE INTERNATIONAL FUZZY SYSTEMS ASSOCIATION AND THE EUROPEAN SOCIETY FOR FUZZY LOGIC AND TECHNOLOGY | 2015年 / 89卷

关键词：

Gradual Patterns; Emerging Patterns; Discriminant Characterisation; CONDENSED REPRESENTATION; CONTRAST SETS; ITEMSETS; RULES;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Mining emerging patterns aims at contrasting data sets and identifying itemsets that characterise a data set by contrast to a reference data set, so as to capture and highlight their differences. This paper considers the case of emerging gradual patterns, to extract discriminant attribute co-variations. It discusses the specific features of these gradual patterns and proposes to transpose an efficient border-based algorithm, justifying its applicability to the gradual case. Illustrative results obtained from a UCI data set are described.

引用

页码：1644 / 1650

页数：7

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