Partitioning fuzzy c-means clustering algorithms for interval-valued data based on city-block distances

被引：4
|
作者：
de Carvalho, Francisco de A. T. [1 ]
Barbosa, Gibson B. N. [1 ]
Pimentel, Julio T. [2 ]
机构：
[1] Univ Fed Pernambuco UFPE, Ctr Informat CIn, Av Jornalista Anibal Fernandes S-N, BR-50740560 Recife, PE, Brazil
[2] Univ Fed Pernambuco UFPE, Dept Engenharia Mecanica, BR-50740560 Recife, PE, Brazil
来源：
2013 BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS) | 2013年
关键词：
fuzzy c-means; interval-valued data; city-block distances;
D O I：
10.1109/BRACIS.2013.27
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents partitioning fuzzy c-means clustering algorithms for interval-valued data based on city-block distaces. These fuzzy c-means clustering algorithms give a fuzzy partition and a prototype for each fuzzy cluster by optimizing an adequacy criterion based on suitable adaptive and non-adaptive city-block distances between vectors of intervals. The adaptive city-block distances change at each algorithm iteration and are different from one fuzzy cluster to another. Experiments with real interval-valued data sets show the usefulness of these fuzzy clustering algorithms.
引用
收藏
页码：113 / 118
页数：6
相关论文
共 50 条
  • [31] Dealing with Distances and Transformations for Fuzzy C-Means Clustering of Compositional Data
    Javier Palarea-Albaladejo
    Josep Antoni Martín-Fernández
    Jesús A. Soto
    [J]. Journal of Classification, 2012, 29 : 144 - 169
  • [32] Fuzzy c-ordered medoids clustering for interval-valued data
    D'Urso, Pierpaolo
    Leski, Jacek M.
    [J]. PATTERN RECOGNITION, 2016, 58 : 49 - 67
  • [33] DATA CLUSTERING BASED ON FUZZY C-MEANS AND CHAOTIC WHALE OPTIMIZATION ALGORITHMS
    Arslan, Hatice
    Toz, Metin
    [J]. SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI, 2019, 37 (04): : 1103 - 1124
  • [34] A Preferential Interval-Valued Fuzzy C-Means Algorithm for Remotely Sensed Imagery Classification
    Guozheng Feng
    Mengying Ni
    Shifeng Ou
    Weiqing Yan
    Jindong Xu
    [J]. International Journal of Fuzzy Systems, 2019, 21 : 2212 - 2222
  • [35] Towards interval-valued fuzzy set-based collaborative fuzzy clustering algorithms
    Long Thanh Ngo
    Trong Hop Dang
    Pedrycz, Witold
    [J]. PATTERN RECOGNITION, 2018, 81 : 404 - 416
  • [36] Federated c-Means and Fuzzy c-Means Clustering Algorithms for Horizontally and Vertically Partitioned Data
    Bárcena, Jose Luis Corcuera
    Marcelloni, Francesco
    Renda, Alessandro
    Bechini, Alessio
    Ducange, Pietro
    [J]. IEEE Transactions on Artificial Intelligence, 2024, 5 (12): : 6426 - 6441
  • [37] A Preferential Interval-Valued Fuzzy C-Means Algorithm for Remotely Sensed Imagery Classification
    Feng, Guozheng
    Ni, Mengying
    Ou, Shifeng
    Yan, Weiqing
    Xu, Jindong
    [J]. INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, 2019, 21 (07) : 2212 - 2222
  • [38] Participatory Learning Fuzzy Clustering for Interval-Valued Data
    Maciel, Leandro
    Ballini, Rosangela
    Gomide, Fernando
    Yager, Ronald R.
    [J]. INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS, IPMU 2016, PT I, 2016, 610 : 687 - 698
  • [39] Exponential distance-based fuzzy clustering for interval-valued data
    D'Urso, Pierpaolo
    Massari, Riccardo
    De Giovanni, Livia
    Cappelli, Carmela
    [J]. FUZZY OPTIMIZATION AND DECISION MAKING, 2017, 16 (01) : 51 - 70
  • [40] Exponential distance-based fuzzy clustering for interval-valued data
    Pierpaolo D’Urso
    Riccardo Massari
    Livia De Giovanni
    Carmela Cappelli
    [J]. Fuzzy Optimization and Decision Making, 2017, 16 : 51 - 70
12345