TY - GEN
T1 - Robust visual mining of data with error information
AU - Sun, Jianyong
AU - Kabán, Ata
AU - Raychaudhury, Somak
PY - 2007
Y1 - 2007
N2 - Recent results on robust density-based clustering have indicated that the uncertainty associated with the actual measurements can be exploited to locate objects that are atypical for a reason unrelated to measurement errors. In this paper, we develop a constrained robust mixture model, which, in addition, is able to nonlinearly map such data for visual exploration. Our robust visual mining approach aims to combine statistically sound density-based analysis with visual presentation of the density structure, and to provide visual support for the identification and exploration of 'genuine' peculiar objects of interest that are not due to the measurement errors. In this model, an exact inference is not possible despite the latent space being discretised, and we resort to employing a structured variational EM. We present results on synthetic data as well as a real application, for visualising peculiar quasars from an astrophysical survey, given photometric measurements with errors.
AB - Recent results on robust density-based clustering have indicated that the uncertainty associated with the actual measurements can be exploited to locate objects that are atypical for a reason unrelated to measurement errors. In this paper, we develop a constrained robust mixture model, which, in addition, is able to nonlinearly map such data for visual exploration. Our robust visual mining approach aims to combine statistically sound density-based analysis with visual presentation of the density structure, and to provide visual support for the identification and exploration of 'genuine' peculiar objects of interest that are not due to the measurement errors. In this model, an exact inference is not possible despite the latent space being discretised, and we resort to employing a structured variational EM. We present results on synthetic data as well as a real application, for visualising peculiar quasars from an astrophysical survey, given photometric measurements with errors.
UR - https://www.scopus.com/pages/publications/38049156399
U2 - 10.1007/978-3-540-74976-9_60
DO - 10.1007/978-3-540-74976-9_60
M3 - 会议稿件
AN - SCOPUS:38049156399
SN - 9783540749752
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 573
EP - 580
BT - Knowledge Discovery in Database
PB - Springer Verlag
T2 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007
Y2 - 17 September 2007 through 21 September 2007
ER -