Abstract
Forward search (FS) methods have been shown to be usefully employed for detecting multiple outliers in continuous multivariate data (Hadi, (1994); Atkinson et al., (2004)). Starting from an outlier-free subset of observations, they iteratively enlarge this good subset using Mahalanobis distances based only on the good observations. In this paper, an alternative formulation of the FS paradigm is presented, that takes a mixture of K > 1 normal components as a null model. The proposal is developed according to both the graphical and the inferential approach to FS-based outlier detection. The performance of the method is shown on an illustrative example and evaluated on a simulation experiment in the multiple cluster setting.
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Calò, D.G. (2008). Mixture Models in Forward Search Methods for Outlier Detection. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_13
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DOI: https://doi.org/10.1007/978-3-540-78246-9_13
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