Algorithms (Oct 2012)

Contextual Anomaly Detection in Text Data

  • Jaideep Srivastava,
  • Nisheeth Srivastava,
  • Amogh Mahapatra

DOI
https://doi.org/10.3390/a5040469
Journal volume & issue
Vol. 5, no. 4
pp. 469 – 489

Abstract

Read online

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.

Keywords