Contextual anomaly detection in text data

Amogh Mahapatra, Nisheeth Srivastava, Jaideep Srivastava

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

We propose using side information to further inform anomaly detection algorithms of the semantic context of the text data they are analyzing, thereby considering both divergence from the statistical pattern seen in particular datasets and divergence seen from more general semantic expectations. Computational experiments show that our algorithm performs as expected on data that reflect real-world events with contextual ambiguity, while replicating conventional clustering on data that are either too specialized or generic to result in contextual information being actionable. These results suggest that our algorithm could potentially reduce false positive rates in existing anomaly detection systems.

Original languageEnglish
Pages (from-to)469-489
Number of pages21
JournalAlgorithms
Volume5
Issue number4
DOIs
Publication statusPublished - 2012
Externally publishedYes

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Keywords

  • Anomaly detection
  • Context detection
  • Topic modeling

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computational Mathematics
  • Numerical Analysis
  • Theoretical Computer Science

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