A Novel Evolutionary Kernel Intuitionistic Fuzzy C -means Clustering Algorithm

This study proposes a novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm (EKIFCM) that combines Atanassov's intuitionistic fuzzy sets (IFSs) with kernel-based fuzzy c-means (KFCM), and genetic algorithms (GA) are optimally used simultaneously to select the parameters of...

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Vydáno v:IEEE transactions on fuzzy systems Ročník 22; číslo 5; s. 1074 - 1087
Hlavní autor: Lin, Kuo-Ping
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.10.2014
Témata:
Algorithm design and analysis
Algorithms
Clustering
Clustering algorithms
Evolutionary
Evolutionary kernel intuitionistic fuzzy c -means (EKIFCM)
Fuzzy
fuzzy c -means (FCM)
Fuzzy logic
Fuzzy set theory
Fuzzy sets
genetic algorithm (GA)
Genetic algorithms
Image segmentation
intuitionistic fuzzy sets
Kernel
kernel function
Kernels
Linear programming
Prototypes
ISSN:1063-6706, 1941-0034
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Shrnutí:This study proposes a novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm (EKIFCM) that combines Atanassov's intuitionistic fuzzy sets (IFSs) with kernel-based fuzzy c-means (KFCM), and genetic algorithms (GA) are optimally used simultaneously to select the parameters of the EKIFCM. The EKIFCM can obtain the advantages of intuitionistic fuzzy sets, kernel functions, and GA in actual clustering problems. Experiments on 2-D synthetic datasets and machine learning repository (http://archive.ics.uci.edu/beta/) datasets show that the proposed EKIFCM is more efficient than conventional algorithms such as the k-means (KM), FCM, Gustafson-Kessel (GK) clustering algorithm, Gath-Geva (GG) clustering algorithm, Chaira's intuitionistic fuzzy c-means (IFCM), and kernel-based fuzzy c-means with Gaussian kernel functions [KFCM(G)] in standard measurement indexes.
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content type line 23
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2013.2280141