Ensemble clustering in the belief functions framework

被引：29
|
作者：
Masson, Marie-Helene [1 ,2 ]
Denoeux, Thierry [1 ,3 ]
机构：
[1] CNRS, Lab Heudiasyc, UMR 6599, F-60205 Compiegne, France
[2] Univ Picardie Jules Verne, IUT Oise, F-60205 Compiegne, France
[3] Univ Technol Compiegne, F-60205 Compiegne, France
来源：
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING | 2011年 / 52卷 / 01期
关键词：
Clustering; Ensemble clustering; Belief functions; Lattice of partitions; Intervals of partitions; CONSENSUS;
D O I：
10.1016/j.ijar.2010.04.007
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, belief functions, defined on the lattice of intervals partitions of a set of objects, are investigated as a suitable framework for combining multiple clusterings. We first show how to represent clustering results as masses of evidence allocated to sets of partitions. Then a consensus belief function is obtained using a suitable combination rule. Tools for synthesizing the results are also proposed. The approach is illustrated using synthetic and real data sets. (C) 2010 Elsevier Inc. All rights reserved.
引用
收藏
页码：92 / 109
页数：18
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