A Novel Evolutionary Kernel Intuitionistic Fuzzy C -means Clustering Algorithm

• Published in: IEEE transactions on fuzzy systems Vol. 22; no. 5; pp. 1074 - 1087
• Main Author: Lin, Kuo-Ping
• Format: Journal Article
• Language: English
• Published: IEEE 01.10.2014
• Subjects: 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
• DOI: 10.1109/TFUZZ.2013.2280141

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.