A causal approach for mining interesting anomalies

Sakshi Babbar, Didi Surian, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We propose a novel approach which combines the use of Bayesian network and probabilistic association rules to discover and explain anomalies in data. The Bayesian network allows us to organize information in order to capture both correlation and causality in the feature space, while the probabilistic association rules have a structure similar to association mining rules. In particular, we focus on two types of rules: (i) low support & high confidence and, (ii) high support & low confidence. New data points which satisfy either one of the two rules conditioned on the Bayesian network are the candidate anomalies. We perform extensive experiments on well-known benchmark data sets and demonstrate that our approach is able to identify anomalies in high precision and recall. Moreover, our approach can be used to discover contextual information from the mined anomalies, which other techniques often fail to do so.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages226-232
Number of pages7
Volume7884 LNAI
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event26th Canadian Conference on Artificial Intelligence, Canadian AI 2013 - Regina, SK
Duration: 28 May 201331 May 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7884 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other26th Canadian Conference on Artificial Intelligence, Canadian AI 2013
CityRegina, SK
Period28/5/1331/5/13

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Keywords

  • anomaly
  • Bayesian network
  • causality
  • probabilistic association rules

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Babbar, S., Surian, D., & Chawla, S. (2013). A causal approach for mining interesting anomalies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7884 LNAI, pp. 226-232). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7884 LNAI). https://doi.org/10.1007/978-3-642-38457-8_20