Abstract
Knowledge discovery techniques had important impact in several relevant application domains. Among the most important knowledge discovery tasks is outlier detection. Outlier detection is the task of identifying anomalous individuals in a given population. This task is very demanding from the computational complexity point of view, being located in the second level of the polynomial hierarchy. Angiulli et al. in 2007 proposed to employ Answer Set Programming (ASP) to compute outliers. Their solution is based on the saturation technique and, as a consequence, it is very hard to evaluate by ASP systems. In this paper we resort to Answer Set Programming with Quantifiers (ASP(Q)) to provide a more declarative, compact and efficient modeling of the outlier detection problem. An experiment on syntetic benchmarks proves that our ASP(Q)-based solution can handle databases that are three order of magnitude larger than the ASP-based one proposed by Angiulli et al.
Partially supported by MISE under project MAP4ID “Multipurpose Analytics Platform 4 Industrial Data”, N. F/190138/01-03/X44.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amendola, G., Ricca, F., Truszczynski, M.: Beyond NP: quantifying over answer sets. Theory Pract. Logic Program. 19(5–6), 705–721 (2019)
Angiulli, F., Fassetti, F., Serrao, C.: ODCA: an outlier detection approach to deal with correlated attributes. In: Golfarelli, M., Wrembel, R., Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) DaWaK 2021. LNCS, vol. 12925, pp. 180–191. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86534-4_17
Angiulli, F., Greco, G., Palopoli, L.: Outlier detection by logic programming. ACM Trans. Comput. Logic 9(1), 7 (2007)
Brewka, G., Eiter, T., Truszczynski, M.: Answer set programming at a glance. Commun. ACM 54(12), 92–103 (2011)
Eiter, T., Gottlob, G.: On the computational cost of disjunctive logic programming: Propositional case. Ann. Math. Artif. Intell. 15(3–4), 289–323 (1995). https://doi.org/10.1007/BF01536399
Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Gener. Comput. 9(3/4), 365–386 (1991). https://doi.org/10.1007/BF03037169
Gupta, M., Gao, J., Aggarwal, C.C., Han, J.: Outlier detection for temporal data: a survey. IEEE Trans. Knowl. Data Eng. 26(9), 2250–2267 (2014)
Maimon, O., Rokach, L. (eds.): Data Mining and Knowledge Discovery Handbook, 2nd edn. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-09823-4
Ord, K.: Outliers in statistical data. Int. J. Forecast. 12(1), 175–176 (1996)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Bellusci, P., Mazzotta, G., Ricca, F. (2022). Modelling the Outlier Detection Problem in ASP(Q). In: Cheney, J., Perri, S. (eds) Practical Aspects of Declarative Languages. PADL 2022. Lecture Notes in Computer Science(), vol 13165. Springer, Cham. https://doi.org/10.1007/978-3-030-94479-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-94479-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-94478-0
Online ISBN: 978-3-030-94479-7
eBook Packages: Computer ScienceComputer Science (R0)