Fast and reliable anomaly detection in categorical data

  title={Fast and reliable anomaly detection in categorical data},
  author={Leman Akoglu and Hanghang Tong and Jilles Vreeken and Christos Faloutsos},
Spotting anomalies in large multi-dimensional databases is a crucial task with many applications in finance, health care, security, etc. We introduce COMPREX, a new approach for identifying anomalies using pattern-based compression. Informally, our method finds a collection of dictionaries that describe the norm of a database succinctly, and subsequently flags those points dissimilar to the norm---with high compression cost---as anomalies. Our approach exhibits four key features: 1) it is… CONTINUE READING
Highly Influential
This paper has highly influenced 11 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 65 citations. REVIEW CITATIONS

11 Figures & Tables



Citations per Year

66 Citations

Semantic Scholar estimates that this publication has 66 citations based on the available data.

See our FAQ for additional information.