Interval Type-2 Relative Entropy Fuzzy C-Means clustering

被引：30
|
作者：
Zarinbal, M. [1 ]
Zarandi, M. H. Fazel [1 ,3 ]
Turksen, I. B. [2 ,3 ]
机构：
[1] Amirkabir Univ Technol, Dept Ind Engn, Tehran, Iran
[2] TOBB Econ & Technol Univ, Ankara, Turkey
[3] Univ Toronto, Knowledge Intelligent Syst Lab, Toronto, ON, Canada
来源：
INFORMATION SCIENCES | 2014年 / 272卷
关键词：
Interval Type-2 fuzzy set theory; Interval arithmetic; Relative entropy; Fuzzy c-means clustering; Interval Type-2 Relative Entropy Fuzzy; C-Means clustering; MEANS ALGORITHM; UNCERTAINTY; CONTROLLERS; REGRESSION; VALUES;
D O I：
10.1016/j.ins.2014.02.066
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Fuzzy set theory especially Type-2 fuzzy set theory provides an efficient tool for handling uncertainties and vagueness in real world observations. Among various clustering techniques, Type-2 fuzzy clustering methods are the most effective methods in the case of having no prior knowledge about observations. While uncertainties in Type-2 fuzzy clustering parameters are investigated by researchers, uncertainties associated with membership degrees are not very well discussed in the literature. In this paper, investigating the latter uncertainties is our concern and Interval Type-2 Relative Entropy Fuzzy C-Means (IT2 REFCM) clustering method is proposed. The computational complexity of the proposed method is discussed and its performance is examined using several experiments. The obtained results show that the proposed method has a very good ability in detecting noises and assignment of suitable membership degrees to observations. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码：49 / 72
页数：24
相关论文
共 50 条
  • [31] Fuzzy C-Means Clustering Interval Type-2 Cerebellar Model Articulation Neural Network for Medical Data Classification
    Le, Tien-Loc
    IEEE ACCESS, 2019, 7 : 20967 - 20973
  • [32] Interval Type-2 Fuzzy Possibilistic C-Means Clustering Based on Granular Gravitational Forces and Particle Swarm Optimization
    Hung Quoc Truong
    Long Thanh Ngo
    Long The Pham
    JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS, 2019, 23 (03) : 592 - 601
  • [33] A Novel Type-2 Fuzzy C-Means Clustering for Brain MR Image Segmentation
    Mishro, Pranaba K.
    Agrawal, Sanjay
    Panda, Rutuparna
    Abraham, Ajith
    IEEE TRANSACTIONS ON CYBERNETICS, 2021, 51 (08) : 3901 - 3912
  • [34] A Multiple Kernels Interval Type-2 Possibilistic C-Means
    Minh Ngoc Vu
    Long Thanh Ngo
    RECENT DEVELOPMENTS IN INTELLIGENT INFORMATION AND DATABASE SYSTEMS, 2016, 642 : 63 - 73
  • [35] A Novel Single Fuzzifier Interval Type-2 Fuzzy C-Means Clustering With Local Information for Land-Cover Segmentation
    Wu, Chengmao
    Guo, Xiaokang
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 : 5903 - 5917
  • [36] A Design of Incremental Granular Model Using Context-Based Interval Type-2 Fuzzy C-Means Clustering Algorithm
    Kwak, Keun-Chang
    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2016, E99D (01): : 309 - 312
  • [37] A modified interval type-2 fuzzy C-means algorithm with application in MR image segmentation
    Qiu, Cunyong
    Xiao, Jian
    Yu, Long
    Han, Lu
    Iqbal, Muhammad Naveed
    PATTERN RECOGNITION LETTERS, 2013, 34 (12) : 1329 - 1338
  • [38] GMKIT2-FCM: A Genetic-based Improved Multiple Kernel Interval Type-2 Fuzzy C-Means Clustering
    Dzung Dinh Nguyen
    Long Thanh Ngo
    Long The Pham
    2013 IEEE INTERNATIONAL CONFERENCE ON CYBERNETICS (CYBCONF), 2013,
  • [39] Improvement of MR Brain Images Segmentation Based On Interval Type-2 Fuzzy C-Means
    Ouarda, Assas
    Fadila, Benmedour
    PROCEEDINGS OF 2015 THIRD IEEE WORLD CONFERENCE ON COMPLEX SYSTEMS (WCCS), 2015,
  • [40] Rough intuitionistic type-2 fuzzy c-means clustering algorithm for MR image segmentation
    Chen, Xiangjian
    Li, Di
    Wang, Xun
    Yang, Xibei
    Li, Hongmei
    IET IMAGE PROCESSING, 2019, 13 (04) : 607 - 614
12345