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
相关论文
共 50 条
  • [1] An Ensemble Clustering Framework Based on Hierarchical Clustering Ensemble Selection and Clusters Clustering
    Li, Wenjun
    Wang, Zikang
    Sun, Wei
    Bahrami, Sara
    [J]. CYBERNETICS AND SYSTEMS, 2023, 54 (05) : 741 - 766
  • [2] A Framework for Hierarchical Ensemble Clustering
    Zheng, Li
    Li, Tao
    Ding, Chris
    [J]. ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, 2014, 9 (02)
  • [3] A New Clustering Ensemble Framework
    Alizadeh, Hosein
    Parvin, Hamid
    Moshki, Mohsen
    Minaei, Behrouz
    [J]. INNOVATIVE COMPUTING TECHNOLOGY, 2011, 241 : 216 - 224
  • [4] Belief functions clustering for epipole localization
    Chen, Huiqin
    Le Hegarat-Mascle, Sylvie
    Aldea, Emanuel
    [J]. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2021, 137 : 146 - 165
  • [5] Classifier Ensemble Framework Based on Clustering
    Parvin, Hamid
    Parvin, Sajad
    Rezaei, Zahra
    Mohamadi, Moslem
    [J]. DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE, 2012, 151 : 743 - 750
  • [6] Clustering of proximity data using belief functions
    Denoeux, T
    Masson, M
    [J]. INTELLIGENT SYSTEMS FOR INFORMATION PROCESSING: FROM REPRESENTATION TO APPLICATIONS, 2003, : 291 - 302
  • [7] An Agglomerative Hierarchical Clustering Framework for Improving the Ensemble Clustering Process
    Jafarzadegan, Mohammad
    Safi-Esfahani, Faramarz
    Beheshti, Zahra
    [J]. CYBERNETICS AND SYSTEMS, 2022, 53 (08) : 679 - 701
  • [8] A pareto ensemble based spectral clustering framework
    Juanjuan Luo
    Huadong Ma
    Dongqing Zhou
    [J]. Complex & Intelligent Systems, 2021, 7 : 495 - 509
  • [9] Clustering ensemble extraction: a knowledge reuse framework
    Sedghi, Mohaddeseh
    Akbari, Ebrahim
    Motameni, Homayun
    Banirostam, Touraj
    [J]. ADVANCES IN DATA ANALYSIS AND CLASSIFICATION, 2024,
  • [10] Clustering Ensemble Framework via Ant Colony
    Parvin, Hamid
    Beigi, Akram
    [J]. ADVANCES IN SOFT COMPUTING, PT II, 2011, 7095 : 153 - 164
12345