文章基本信息

标题：Data Clustering Method Using a Modified Gaussian Kernel Metric and Kernel PCA
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作者：Lee, Hansung ; Yoo, Jang-Hee ; Park, Daihee 等
期刊名称：ETRI Journal
印刷版ISSN：1225-6463
电子版ISSN：2233-7326
出版年度：2014
卷号：36
期号：3
页码：333-342
DOI：10.4218/etrij.14.0113.0553
语种：English
出版社：Electronics and Telecommunications Research Institute
摘要：Most hyper-ellipsoidal clustering (HEC) approaches use the Mahalanobis distance as a distance metric. It has been proven that HEC, under this condition, cannot be realized since the cost function of partitional clustering is a constant. We demonstrate that HEC with a modified Gaussian kernel metric can be interpreted as a problem of finding condensed ellipsoidal clusters (with respect to the volumes and densities of the clusters) and propose a practical HEC algorithm that is able to efficiently handle clusters that are ellipsoidal in shape and that are of different size and density. We then try to refine the HEC algorithm by utilizing ellipsoids defined on the kernel feature space to deal with more complex-shaped clusters. The proposed methods lead to a significant improvement in the clustering results over K-means algorithm, fuzzy C-means algorithm, GMM-EM algorithm, and HEC algorithm based on minimum-volume ellipsoids using Mahalanobis distance.
关键词：Data clustering;hyper-ellipsoidal clustering;minimum-volume ellipsoids;kernel PCA;Gaussian kernel