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|Title:||Fuzzy clustering using multiple Gaussian kernels with optimized-parameters||Authors:||Dagher, Issam||Affiliations:||Department of Computer Engineering||Keywords:||Fuzzy
|Issue Date:||2018||Part of:||Journal of fuzzy optimization and decision making||Volume:||17||Issue:||2||Start page:||159||End page:||176||Abstract:||
In this paper, we propose a new kernel-based fuzzy clustering algorithm which tries to find the best clustering results using optimal parameters of each kernel in each cluster. It is known that data with nonlinear relationships can be separated using one of the kernel-based fuzzy clustering methods. Two common fuzzy clustering approaches are: clustering with a single kernel and clustering with multiple kernels. While clustering with a single kernel doesnt work well with "multiple-density" clusters, multiple kernel-based fuzzy clustering tries to find an optimal linear weighted combination of kernels with initial fixed (not necessarily the best) parameters. Our algorithm is an extension of the single kernel-based fuzzy c-means and the multiple kernel-based fuzzy clustering algorithms. In this 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. In order to show its effective clustering performance, we have compared it to other similar clustering algorithms using different databases and different clustering validity measures.
|URI:||https://scholarhub.balamand.edu.lb/handle/uob/2028||Ezproxy URL:||Link to full text||Type:||Journal Article|
|Appears in Collections:||Department of Computer Engineering|
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