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Automated clustering algorithms for classification of astronomical objects
Zhang, Y; Zhao, Y
AbstractData mining is an important and challenging problem for the efficient analysis of large astronomical databases and will become even more important with the development of the Global Virtual Observatory. In this study, learning vector quantization (LVQ), single-layer perceptron (SLP) and support vector machines (SVM) were used for multi-wavelength data classification. A feature selection technique was used to evaluate the significance of the considered features for the results of classification. We conclude that in the situation of fewer features, LVQ and SLP show better performance. In contrast, SVM shows better performance when considering more features. The focus of the automatic classification is on the development of an efficient feature-based classifier. The classifiers trained by these methods can be used to preselect AGN candidates.
KeywordMethods : Data Analysis Methods : Statistical Astronomical Data Bases : Miscellaneous Catalogs
Indexed BySCI
WOS IDWOS:000223659500037
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Cited Times:45[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
AffiliationChinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Y,Zhao, Y. Automated clustering algorithms for classification of astronomical objects[J]. ASTRONOMY & ASTROPHYSICS,2004,422(3):1113-1121.
APA Zhang, Y,&Zhao, Y.(2004).Automated clustering algorithms for classification of astronomical objects.ASTRONOMY & ASTROPHYSICS,422(3),1113-1121.
MLA Zhang, Y,et al."Automated clustering algorithms for classification of astronomical objects".ASTRONOMY & ASTROPHYSICS 422.3(2004):1113-1121.
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