Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/584
Title: Fuzzy clustering with optimized-parameters multiple Gaussian Kernels
Authors: Dagher, Issam 
Affiliations: Department of Computer Engineering 
Keywords: Kernel
Clustering algorithms
Prototype
Symmetric matrices
Spirals
Subjects: Integrated circuits
Databases
Issue Date: 2015
Publisher: IEEE
Part of: IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2015
Start page: 1
End page: 6
Conference: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2-5 Aug. 2015 : Istanbul, Turkey) 
Abstract: 
Kernel fuzzy clustering has been applied to data with nonlinear relationships. Two approaches were used: clustering with a single kernel and clustering with multiple kernels. While clustering with a single kernel doesn't work well with "multiple-density" clusters, Multiple Kernel Fuzzy clustering tries to find an optimal linear weighted combination of kernels with initial fixed (not necessarily the best) parameters. Our algorithm tries to find the optimal parameters of each kernel in each cluster. It is a generalization of the single Kernel Fuzzy c-means and the Multiple Kernel Fuzzy clustering algorithms. In our algorithm, there is no need to give "good" parameters of each kernel and no need to give an initial "good" number of kernels. Every cluster will be characterized by a Gaussian Kernel with optimal parameters. Using different databases and different clustering validity measures, it is compared to other similar clustering algorithms showing its effective clustering performance.
URI: https://scholarhub.balamand.edu.lb/handle/uob/584
Ezproxy URL: Link to full text
Type: Conference Paper
Appears in Collections:Department of Computer Engineering

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