Outlier detection using default reasoning

Fabrizio Angiulli, Rachel Ben-Eliyahu - Zohary, Luigi Palopoli

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Default logics are usually used to describe the regular behavior and normal properties of domain elements. In this paper we suggest, conversely, that the framework of default logics can be exploited for detecting outliers. Outliers are observations expressed by sets of literals that feature unexpected semantical characteristics. These sets of literals are selected among those explicitly embodied in the given knowledge base. Hence, essentially we perceive outlier detection as a knowledge discovery technique. This paper defines the notion of outlier in two related formalisms for specifying defaults: Reiter's default logic and extended disjunctive logic programs. For each of the two formalisms, we show that finding outliers is quite complex. Indeed, we prove that several versions of the outlier detection problem lie over the second level of the polynomial hierarchy. We believe that a thorough complexity analysis, as done here, is a useful preliminary step towards developing effective heuristics and exploring tractable subsets of outlier detection problems.

Original languageEnglish
Pages (from-to)1837-1872
Number of pages36
JournalArtificial Intelligence
Volume172
Issue number16-17
DOIs
StatePublished - 1 Nov 2008

Keywords

  • Computational complexity
  • Data mining
  • Default logic
  • Disjunctive logic programming
  • Knowledge representation
  • Nonmonotonic reasoning
  • Outlier detection

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