A Semi-Supervised Fuzzy Clustering Algorithm Based on Mahalanobis Distance and Gaussian Kernel

Kai Li; Yu Fei Zhou; Xiao Juan Li

doi:10.4028/www.scientific.net/AMM.385-386.1513

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 385-386A Semi-Supervised Fuzzy Clustering Algorithm Based...

A Semi-Supervised Fuzzy Clustering Algorithm Based on Mahalanobis Distance and Gaussian Kernel

Abstract:

Semi-supervised clustering is a method which can improve clustering performance by introducing partial supervised information. This paper mainly studies the semi-supervised fuzzy clustering which introduces Mahalanobis distance and Gaussian Kernel. And we obtain a new semi-supervised fuzzy clustering objective function. By solving the optimization problem, we propose a semi-supervised fuzzy clustering algorithm F-SCAPC which includes F(M)-SCAPC and F(K)-SCAPC. And we do experimental research for proposed algorithm F-SCAPC using the selected standard data set and the artificial data set. Besides, we compare performance of presented algorithm F-SCAPC with AFFC, KFCM-F and SCAPC algorithms. From the results, we can see that F-SCAPC is effective in the convergence speed and the accuracy.

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Periodical:

Applied Mechanics and Materials (Volumes 385-386)

Pages:

1513-1516

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.385-386.1513

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Online since:

August 2013

Authors:

Kai Li, Yu Fei Zhou, Xiao Juan Li

Keywords:

Gaussian Kernel, Mahalanobis Distance, Pairwise Constraints, Semi-Supervised Clustering

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