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 39 条
  • [31] Pedrycz W, 2005, KNOWLEDGE-BASED CLUSTERING: FROM DATA TO INFORMATION GRANULES, P1, DOI 10.1002/0471708607
  • [32] Pedrycz W., 2013, Granular Computing: Analysis and Design of Intelligent Systems
  • [33] Raza M., 2012, P IEEE INT C FUZZ SY, P1
  • [34] Uncertainty measures for interval type-2 fuzzy sets
    Wu, Dongrui
    Mendel, Jerry M.
    [J]. INFORMATION SCIENCES, 2007, 177 (23) : 5378 - 5393
  • [35] A type-2 fuzzy c-regression clustering algorithm for Takagi-Sugeno system identification and its application in the steel industry
    Zarandi, M. H. Fazel
    Gamasaee, R.
    Turksen, I. B.
    [J]. INFORMATION SCIENCES, 2012, 187 : 179 - 203
  • [36] Systematic image processing for diagnosing brain tumors: A Type-II fuzzy expert system approach
    Zarandi, M. H. Fazel
    Zarinbal, M.
    Izadi, M.
    [J]. APPLIED SOFT COMPUTING, 2011, 11 (01) : 285 - 294
  • [37] Zarandi MHF, 2009, PROCEEDINGS OF THE JOINT 2009 INTERNATIONAL FUZZY SYSTEMS ASSOCIATION WORLD CONGRESS AND 2009 EUROPEAN SOCIETY OF FUZZY LOGIC AND TECHNOLOGY CONFERENCE, P30
  • [38] Relative entropy fuzzy c-means clustering
    Zarinbal, M.
    Zarandi, M. H. Fazel
    Turksen, I. B.
    [J]. INFORMATION SCIENCES, 2014, 260 : 74 - 97
  • [39] A novel approach to multi attribute group decision making based on trapezoidal interval type-2 fuzzy soft sets
    Zhang, Zhiming
    Zhang, Shouhua
    [J]. APPLIED MATHEMATICAL MODELLING, 2013, 37 (07) : 4948 - 4971
1234