Title :
Interval type-2 fuzzy c-means clustering using intuitionistic fuzzy sets
Author :
Dzung Dinh Nguyen ; Long Thanh Ngo ; Long The Pham
Author_Institution :
Dept. of Inf. Syst., Le Quy Don Tech. Univ., Hanoi, Vietnam
Abstract :
In this paper, intuitionistic interval type-2 fuzzy c-means clustering (InIT2FCM) method is proposed for the clustering problems. Intuitionistic fuzzy sets (IFS) and intuitionistic type-2 fuzzy sets (InIT2FS) were introduced with the aim to better handle the uncertainty. Utilizing the advantages of the IFS and InT2FS, we have combined them with fuzzy clustering algorithms to overcome some drawbacks of the “conventional” FCM in handling uncertainty. The experiments were completed for different types of images which show the advantages of the proposed algorithms, especially with noisy images.
Keywords :
fuzzy set theory; image processing; pattern clustering; IFS; InIT2FCM method; InIT2FS; InT2FS; intuitionistic interval type-2 fuzzy c-means clustering algorithm; intuitionistic type-2 fuzzy sets; noisy images; uncertainty handling; Integrated circuits; Uncertainty; Intuitionistic fuzzy c-means clustering; Intuitionistic fuzzy sets; intuitionistic type-2 fuzzy sets; type-2 fuzzy c-means clustering;
Conference_Titel :
Information and Communication Technologies (WICT), 2013 Third World Congress on
Conference_Location :
Hanoi
DOI :
10.1109/WICT.2013.7113152