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Journal Article
Data Clustering Method Using a Modified Gaussian Kernel Metric and Kernel PCA
- Authors
- Hansung Lee, Jang-Hee Yoo, Daihee Park
- Issue Date
- 2014-06
- Citation
- ETRI Journal, v.36, no.3, pp.333-342
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- 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 Cmeans algorithm, GMM-EM algorithm, and HEC algorithm based on minimum-volume ellipsoids using Mahalanobis distance. © 2014 ETRI.
- KSP Keywords
- Clustering method, Cost Function, Data clustering, Different sizes, Distance metric, EM Algorithm, Feature space, GMM-EM, Gaussian kernel, Kernel Principal Component Analysis(KPCA), Partitional clustering