Automated Clustering Algorithms for Classification of Astronomical   Objects
  Data 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 put forward for multi-wavelength data classification. A feature selection technique was used to evaluate the significance of the considered features to the results of classification. From the results, we conclude that in the situation of less 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 efficient feature-based classifier. The classifiers trained by these methods can be used for preselecting AGN candidates. 
Yanxia Zhang, Yongheng Zhao (National Astronomical Observatories,   Chinese Academy of Sciences)