Contextual anomaly detection in text data

Amogh Mahapatra, Nisheeth Srivastava, Jaideep Srivastava

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

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 (US)
Pages (from-to)469-489
Number of pages21
JournalAlgorithms
Volume5
Issue number4
DOIs
StatePublished - 2012

Keywords

  • Anomaly detection
  • Context detection
  • Topic modeling

Fingerprint Dive into the research topics of 'Contextual anomaly detection in text data'. Together they form a unique fingerprint.

Cite this