Complexity and expressive power of logic programming
ACM Computing Surveys (CSUR)
Enhancing Disjunctive Datalog by Constraints
IEEE Transactions on Knowledge and Data Engineering
Smodels - An Implementation of the Stable Model and Well-Founded Semantics for Normal LP
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
The DLV system for knowledge representation and reasoning
ACM Transactions on Computational Logic (TOCL)
Outlier detection by logic programming
ACM Transactions on Computational Logic (TOCL)
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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 LP-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 LP-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.