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
  • [1] Uncertain fuzzy clustering:: Interval type-2 fuzzy approach to C-means
    Hwang, Cheul
    Rhee, Frank Chung-Hoon
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2007, 15 (01) : 107 - 120
  • [2] Interval Type-2 Fuzzy C-Means Approach to Collaborative Clustering
    Trong Hop Dang
    Long Thanh Ngo
    Pedrycz, Witold
    2015 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2015), 2015,
  • [3] Interval Type-2 Fuzzy Possibilistic C-Means Clustering Algorithm
    Rubio, E.
    Castillo, Oscar
    Melin, Patricia
    RECENT DEVELOPMENTS AND NEW DIRECTION IN SOFT-COMPUTING FOUNDATIONS AND APPLICATIONS, 2016, 342 : 185 - 194
  • [4] Genetic Based Interval Type-2 Fuzzy C-Means Clustering
    Dzung Dinh Nguyen
    Long Thanh Ngo
    Long The Pham
    CONTEXT-AWARE SYSTEMS AND APPLICATIONS, (ICCASA 2012), 2013, 109 : 239 - 248
  • [5] Multiple Kernel Interval Type-2 Fuzzy C-Means Clustering
    Dzung Dinh Nguyen
    Long Thanh Ngo
    2013 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ - IEEE 2013), 2013,
  • [6] Interval Type-2 Fuzzy C-means Clustering using Intuitionistic Fuzzy Sets
    Dzung Dinh Nguyen
    Long Thanh Ngo
    Long The Pham
    2013 THIRD WORLD CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGIES (WICT), 2013, : 299 - 304
  • [7] Relative entropy fuzzy c-means clustering
    Zarinbal, M.
    Zarandi, M. H. Fazel
    Turksen, I. B.
    INFORMATION SCIENCES, 2014, 260 : 74 - 97
  • [8] A new Interval Type-2 Fuzzy Possibilistic C-Means Clustering Algorithm
    Rubio, E.
    Castillo, O.
    Melin, P.
    2015 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY DIGIPEN NAFIPS 2015, 2015,
  • [9] A type-2 fuzzy C-means clustering algorithm
    Rhee, FCH
    Hwang, C
    JOINT 9TH IFSA WORLD CONGRESS AND 20TH NAFIPS INTERNATIONAL CONFERENCE, PROCEEDINGS, VOLS. 1-5, 2001, : 1926 - 1929
  • [10] Robust interval type-2 possibilistic C-means clustering
    Yu, Long
    Xiao, Jian
    Zhou, Cong
    Kongzhi yu Juece/Control and Decision, 2009, 24 (04): : 503 - 507
12345