Group deviation detection methods: A survey

Edward Toth, Sanjay Chawla

Research output: Contribution to journalReview article

2 Citations (Scopus)

Abstract

Pointwise anomaly detection and change detection focus on the study of individual data instances; however, an emerging area of research involves groups or collections of observations. From applications of high-energy particle physics to health care collusion, group deviation detection techniques result in novel research discoveries, mitigation of risks, prevention of malicious collaborative activities, and other interesting explanatory insights. In particular, static group anomaly detection is the process of identifying groups that are not consistent with regular group patterns, while dynamic group change detection assesses significant differences in the state of a group over a period of time. Since both group anomaly detection and group change detection share fundamental ideas, this survey article provides a clearer and deeper understanding of group deviation detection research in static and dynamic situations.

Original languageEnglish
Article number3203246
JournalACM Computing Surveys
Volume51
Issue number4
DOIs
Publication statusPublished - 1 Jul 2018

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Keywords

  • Discriminative methods
  • Generative models
  • Group anomaly detection
  • Group change detection
  • Group deviation detection
  • Hypothesis testing
  • Machine learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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