Robust visual mining of data with error information

Abstract

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. © Springer-Verlag Berlin Heidelberg 2007.