Outlier detection using default reasoning

Outlier Detection Using Default Logic

Default logic is used to describe regular behavior and normal properties. We suggest to exploit the framework of default logic for detecting outliers individuals who behave in an unexpected way or feature abnormal properties. The ability to locate outliers can help to maintain knowledgebase integrity and to single out irregular individuals. We first formally define the notion of an outlier and ...

Default Reasoning Using Classical Logic

In this paper we show how propositional default theories can be characterized by classical propositional theories: for each nite default theory, we show a classical propositional theory such that there is a one-to-one correspondence between models for the latter and extensions of the former. This means that computing extensions and answering queries about coherence, set-membership and set-entai...

Outlier detection in default logics: the tractability/intractability frontier

In knowledge bases expressed in default logic, outliers are sets of literals, or observations, that feature unexpected properties. This paper introduces the notion of strong outliers and studies the complexity problems related to outlier recognition in the fragment of acyclic normal unary theories and the related one of mixed unary theories. We show that recognizing strong outliers in acyclic n...

Outlier Detection Using SemiDiscrete Decomposition

Semidiscrete decomposition (SDD) is usually presented as a storage-eecient analogue of singular value decomposition. We show, however, that SDD actually works in a completely diierent way, and is best thought of as a bump-hunting technique; it is extremely eeective at nding outlier clusters in datasets. We suggest that SDD's success in text retrieval applications such as latent semantic indexin...

Outlier Detection using Graph Mining