Kernel intuitionistic fuzzy c-means and state transition algorithm for clustering problem-Reference-Cited by-同舟云学术

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Kernel intuitionistic fuzzy c-means and state transition algorithm for clustering problem

Published:2020-04-02 Issue:20 Volume:24 Page:15507-15518
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Zhou Xiaojun,Zhang Rundong,Wang Xiangyue,Huang Tingwen,Yang Chunhua

Funder

National Natural Science Foundation of China

Innovation-Driven Plan in Central South University

111 Project

Hunan Provincial Natural Science Foundation of China

Fundamental Research Funds for the Central Universities of Central South University

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s00500-020-04879-8.pdf

Reference45 articles.

1. Askari S, Montazerin N, Zarandi MH et al (2017) Generalized entropy based possibilistic fuzzy C-Means for clustering noisy data and its convergence proof. Neurocomputing 219:186–202

2. Atanassov KT, Rangasamy P (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96

3. Babu GP, Murty MN (1993) A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm. Pattern Recognit Lett 14(10):763–769

4. Bastanlar Y, Ozuysal M (2014) Introduction to machine learning. Methods Mol Biol 1107:105–128

5. Bezdek JC, Ehrlich R, Full WE et al (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10:191–203

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