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Unsupervised Possibilistic clustering Based on Kernel Methods

Physics Procedia 2012年 25卷 1084-1090页

作者： Yating Hu Chuncheng Zuo Fuheng Qu Weili Shi School of Information and Technology Jilin Agriculture University Changchun China School of Mechanical Science and Engineering Jilin University Changchun China School of Computer Science and Technology Changchun University of Sci. and Tech

A new kernel based unsupervised clustering algorithm has been proposed. The proposed algorithm is called unsupervised kernel possibilistic clustering algorithm (UKPC), which is an extension of the previously proposed clustering algorithm of unsupervised possibilistic clustering algorithm (UPC). In UKPC, the sample points are mapped into the feature space by the introduced kernel function, and the final clustering partition is obtained by optimizing the objective function of UKPC, which adopts the same clustering rule with UPC clustering model. UKPC has the ability of revealing the non-convex cluster structure because the input data are mapped implicitly into a high-dimensional feature space where the nonlinear pattern now appears linear. The contrast experimental results with UPC and other typical fuzzy clustering algorithms show the better performance of the proposed algorithm.

关键词： fuzzy clustering possibilistic clustering non-convex cluster structure unsupervised possibilistic clustering feature space

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Unsupervised Possibilistic clustering Based on Kernel Methods

Unsupervised Possibilistic Clustering Based on Kernel Method...

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2012 International Conference on Solid State Devices and Materials Science(SSDMS 2012)

作者： Yating Hu Chuncheng Zuo Fuheng Qu Weili Shi School of Mechanical Science and Engineering Jilin University School of Computer Science and Technology Changchun Univ.of Sci.and Tech.

A new kernel based unsupervised clustering algorithm has been proposed. The proposed algorithm is called unsupervised kernel possibilistic clustering algorithm(UKPC), which is an extension of the previously proposed clustering algorithm of unsupervised possibilistic clustering algorithm(UPC). In UKPC, the sample points are mapped into the feature space by the introduced kernel function, and the final clustering partition is obtained by optimizing the objective function of UKPC, which adopts the same clustering rule with UPC clustering model. UKPC has the ability of revealing the non-convex cluster structure because the input data are mapped implicitly into a high-dimensional feature space where the nonlinear pattern now appears linear. The contrast experimental results with UPC and other typical fuzzy clustering algorithms show the better performance of the proposed algorithm.

关键词： fuzzy clustering possibilistic clustering non-convex cluster structure unsupervised possibilistic clustering feature space

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Bearing Fault Diagnosis Based on Kernel Possibilistic clustering Algorithm

Bearing Fault Diagnosis Based on Kernel Possibilistic Cluste...

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2011 International Conference on Future Software Engineering and Multimedia Engineering(FSME 2011)

作者： Yating Hu Chuncheng Zuo Fuheng Qu School of Mechanical Science and Engineering Jilin University School of Information and Technology Jilin Agriculture University School of Computer Science and Technology Changchun Univ. of Sci. and Tech

The vibration signals of mechanical bearings and components are employed in analyzing the status of their work, which is a commonly used method in the study of mechanical fault monitoring and diagnosisAiming at solving the problems of possibilistic clustering algorithm in applying into the fault diagnosis, we combines kernel function with possibilistic clustering to get an unsupervised kernel possibilistic clustering algorithm(UKPC)UKPC has the ability of revealing the non-convex cluster structures, and it has been applied to bearing fault diagnosisThe experimental result shows UKPC can effectively diagnosis the fault of rolling bearings.

关键词： fault diagnosis possibilistic clustering non-convex cluster structure kernel function

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