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학술지 Data Clustering Method Using a Modified Gaussian Kernel Metric and Kernel PCA
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저자
이한성, 유장희, 박대희
발행일
201406
출처
ETRI Journal, v.36 no.3, pp.333-342
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.14.0113.0553
협약과제
13VS1100, 사람에 의한 안전위협의 실시간 인지를 위한 능동형 영상보안 서비스용 원거리 (CCTV 주간환경 5m이상) 사람 식별 및 검색 원천기술 개발, 유장희
초록
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.
키워드
Data clustering, Gaussian kernel, Hyper-ellipsoidal clustering, Kernel PCA, Minimum-volume ellipsoids
KSP 제안 키워드
Clustering method, Cost Function, Data clustering, Different sizes, Distance metric, EM Algorithm, Feature space, GMM-EM, Gaussian kernel, K-means algorithm, Partitional clustering