Kernelized Fuzzy Fisher Criterion based Clustering Algorithm
- Resource Type
- Conference
- Authors
- Cao, Su-Qun; Hou, Zhi-Wei; Wang, Liu-Yang; Zhu, Quan-Yin
- Source
- 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science Distributed Computing and Applications to Business Engineering and Science (DCABES), 2010 Ninth International Symposium on. :87-91 Aug, 2010
- Subject
- Computing and Processing
Communication, Networking and Broadcast Technologies
General Topics for Engineers
Clustering algorithms
Kernel
Algorithm design and analysis
Eigenvalues and eigenfunctions
Artificial neural networks
Classification algorithms
Computational modeling
fuzzy Fisher criterion
kernel methods
fuzzy clustering
- Language
Fuzzy Fisher Criterion(FFC) based clustering method uses the fuzzy Fisher’s linear discriminant(FLD) as its clustering objective function and is more robust to noises and outliers than fuzzy c-means clustering(FCM). But FFC can only be used in linear separable dataset. In this paper, a novel fuzzy clustering algorithm, called Kernelized Fuzzy Fisher Criterion(KFFC) based clustering algorithm, is proposed. With kernel methods KFFC can perform clustering in kernel feature space while FFC makes clustering in Euclidean space. The experimental results show that the proposed algorithm can deal with the linear non-separable problem better than FFC.