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In this paper we present an unsupervised distance-based outlier detection method designed to learn a model over the objects contained in a data set. The learned model, called <i>solving set</i>, is a small subset of the data set that is used to classify new unseen objects as outliers or not. We provide an algorithm that computes a solving set with(More)
definitiva in altra sede. ABSTRACT Planning adequate audit strategies is a key success factor in a posteriori fraud detection applications, such as in fiscal and insurance domains, where audits are intended to detect fraudulent behavior. In this paper we describe an experience resulting from the collaboration among Data Mining researchers, domain experts of(More)