Belief hierarchical clustering

被引：5

作者：

Maalel, Wiem ^{[1
,2
]}

Zhou, Kuang ^{[1
,2
]}

Martin, Arnaud ^{[1
,2
]}

Elouedi, Zied ^{[1
,2
]}

机构：

[1] LARODEC, ISG.41 rue de la Libert´e

[2] IRISA, Universit´e de Rennes 1, IUT de Lannion, Rue Edouard Branly BP 30219, Lannion cedex

来源：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2014年 / 8764卷

关键词：

Belief clustering; Belief function; Clustering; Hierarchical clustering;

D O I：

10.1007/978-3-319-11191-9_8

中图分类号：

学科分类号：

摘要：

In the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases. This paper deals with a new approach to cluster uncertain data by using a hierarchical clustering defined within the belief function framework. The main objective of the belief hierarchical clustering is to allow an object to belong to one or several clusters. To each belonging, a degree of belief is associated, and clusters are combined based on the pignistic properties. Experiments with real uncertain data show that our proposed method can be considered as a propitious tool. © Springer International Publishing Switzerland 2014.

引用

页码：68 / 76

页数：8

共 12 条

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