Abstract
The development of effective knowledge discovery techniques has become a very active research area in recent years due to the important impact it has had in several relevant application domains. One interesting task therein is that of singling out anomalous individuals from a given population, e.g., to detect rare events in time-series analysis settings, or to identify objects whose behavior is deviant w.r.t. a codified standard set of rules. Such exceptional individuals are usually referred to as outliers in the literature.
In this paper, the \(\mathcal{LP\mbox{-\,}OD}\) logic programming outlier detection system is described, based on the concept of outlier formally stated in the context of knowledge-based systems in [1]. The \(\mathcal{LP\mbox{-\,}OD}\) system exploits a rewriting algorithm that transforms any outlier detection problem into an equivalent inference problem under stable model semantics, thereby making outlier computation effective and realizable on top of any stable model solver.
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Angiulli, F., Greco, G., Palopoli, L., Trimboli, D. (2007). The \(\mathcal{LP\mbox{-\,}OD}\) System: Logic Programming Meets Outlier Detection. In: Baral, C., Brewka, G., Schlipf, J. (eds) Logic Programming and Nonmonotonic Reasoning. LPNMR 2007. Lecture Notes in Computer Science(), vol 4483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72200-7_22
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DOI: https://doi.org/10.1007/978-3-540-72200-7_22
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