Fitting mixtures of regression lines with the forward search: application to clustering and outlier detection
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The forward search is a method for detecting unidentified subsets and masked outliers and for determining their effect on models fitted to the data. This talk describes a semi-automatic approach to outlier detection and clustering through the forward search. We address challenging issues including selection of the number of groups. The performance of the algorithm is shown on several trade data sets relevant for fraud detection problems.
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