Reliability-based fuzzy clustering ensemble

被引:48
作者
Bagherinia, Ali [1 ]
Minaei-Bidgoli, Behrooz [2 ]
Hosseinzadeh, Mehdi [3 ,4 ]
Parvin, Hamid [5 ,6 ]
机构
[1] Islamic Azad Univ, Sci & Res Branch, Dept Comp Engn, Tehran, Iran
[2] Iran Univ Sci & Technol, Comp Engn Dept, Tehran, Iran
[3] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
[4] Iran Univ Med Sci, Hlth Management & Econ Res Ctr, Tehran, Iran
[5] Islamic Azad Univ, Mamasani Branch, Dept Engn, Mamasani, Iran
[6] Islamic Azad Univ, Nourabad Mamasani Branch, Young Researchers & Elite Club, Nourabad Mamasani, Iran
关键词
Fuzzy clustering ensemble; Fuzzy cluster reliability; Co-association matrix; Consensus function; C-MEANS; VALIDITY; SCHEME;
D O I
10.1016/j.fss.2020.03.008
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In the clustering ensemble the quality of base-clusterings influences the consensus clustering. Although some researches have been devoted to weighting the base-clustering, fuzzy cluster level weighting has been ignored, more specifically, they did not pay attention to the role of cluster reliability in the fuzzy clustering ensemble. In this paper, we propose a new fuzzy clustering ensemble framework without access to the features of data-objects based on fuzzy cluster-level weighting. The reliability of each fuzzy cluster is computed based on estimation of its unreliability, and is considered as its weight in the ensemble. The unreliability of fuzzy clusters is estimated by applying the similarity between fuzzy clusters in the ensemble based on an entropic criterion. In our framework, the final clustering is produced by two types of consensus functions: (1) a reliability-based weighted fuzzy co-association matrix is constructed from the base-clusterings and then, a single traditional clustering such as hierarchical agglomerative clustering or K-means is applied over the matrix to produce the final clustering. (2) a new graph based fuzzy consensuses function. The graph based consensus function has linear time complexity in the number of data-objects. Experimental results on various standard datasets demonstrated the effectiveness of the proposed approach compared to the state-of-the-art methods in terms of evaluation criteria and clustering robustness. (c) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页码:1 / 28
页数:28
相关论文
共 59 条
  • [1] Alcalá-Fdez J, 2011, J MULT-VALUED LOG S, V17, P255
  • [2] OPTIMIZING FUZZY CLUSTER ENSEMBLE IN STRING REPRESENTATION
    Alizadeh, Hosein
    Minaei-Bidgoli, Behrouz
    Parvin, Hamid
    [J]. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2013, 27 (02)
  • [3] Comparing Fuzzy, Probabilistic, and Possibilistic Partitions
    Anderson, Derek T.
    Bezdek, James C.
    Popescu, Mihail
    Keller, James M.
    [J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2010, 18 (05) : 906 - 918
  • [4] Fuzzy ensemble clustering based on random projections for DNA microarray data analysis
    Avogadri, Roberto
    Valentini, Giorgio
    [J]. ARTIFICIAL INTELLIGENCE IN MEDICINE, 2009, 45 (2-3) : 173 - 183
  • [5] Ball GH, 1965, ISODATA NOVEL METHOD
  • [6] A heterogeneous cluster ensemble model for improving the stability of fuzzy cluster analysis
    Bedalli, Erind
    Mancellari, Enea
    Asilkan, Ozcan
    [J]. 12TH INTERNATIONAL CONFERENCE ON APPLICATION OF FUZZY SYSTEMS AND SOFT COMPUTING, ICAFS 2016, 2016, 102 : 129 - 136
  • [7] Berikov V. B., 2018, Pattern Recognition and Image Analysis, V28, P1
  • [8] FCM - THE FUZZY C-MEANS CLUSTERING-ALGORITHM
    BEZDEK, JC
    EHRLICH, R
    FULL, W
    [J]. COMPUTERS & GEOSCIENCES, 1984, 10 (2-3) : 191 - 203
  • [9] Blake C. L., 1998, 55 U CAL DEP INF COM, V55
  • [10] Caliski T., 1974, COMMUN STAT, V3, P1, DOI [10.1080/03610927408827101, DOI 10.1080/03610927408827101]