Rough multi-category decision theoretic framework

被引：0

作者：

Lingras, Pawan ^{[1
]}

Chen, Min ^{[1
,2
]}

Miao, Duoqian ^{[2
]}

机构：

[1] St Marys Univ, Dept Math & Comp Sci, Halifax, NS B3H 3C3, Canada

[2] Shanghai Univ, Sch Elect & Informat Engn, Shanghai 201804, Peoples R China

来源：

ROUGH SETS AND KNOWLEDGE TECHNOLOGY | 2008年 / 5009卷

基金：

加拿大自然科学与工程研究理事会;

关键词：

rough sets; web usage mining; rough approximation; k-means cluster algorithm;

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：

Decision theoretic framework has been helpful in providing a better understanding of classification models. In particular, decision theoretic interpretations of different types of the binary rough set classification model have led to the refinement of these models. This study extends the decision theoretic rough set model to supervised and unsupervised multi-category problems. The proposed framework can be used to study the multi-classification and clustering problems within the context of rough set theory.

引用

页码：676 / +

页数：2

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