Multivariate voronoi outlier detection for time series

Chris E. Zwilling, Michelle Yongmei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Outlier detection is a primary step in many data mining and analysis applications, including healthcare and medical research. This paper presents a general method to identify outliers in multivariate time series based on a Voronoi diagram, which we call Multivariate Voronoi Outlier Detection (MVOD). The approach copes with outliers in a multivariate framework, via designing and extracting effective attributes or features from the data that can take parametric or nonparametric forms. Voronoi diagrams allow for automatic configuration of the neighborhood relationship of the data points, which facilitates the differentiation of outliers and non-outliers. Experimental evaluation demonstrates that our MVOD is an accurate, sensitive, and robust method for detecting outliers in multivariate time series data.

Original languageEnglish (US)
Title of host publication2014 IEEE Healthcare Innovation Conference, HIC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages300-303
Number of pages4
ISBN (Electronic)9781467363648
DOIs
StatePublished - Feb 10 2014
Event2014 IEEE Healthcare Innovation Conference, HIC 2014 - Seattle, United States
Duration: Oct 8 2014Oct 10 2014

Publication series

Name2014 IEEE Healthcare Innovation Conference, HIC 2014

Other

Other2014 IEEE Healthcare Innovation Conference, HIC 2014
CountryUnited States
CitySeattle
Period10/8/1410/10/14

ASJC Scopus subject areas

  • Medicine(all)
  • Biomedical Engineering

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  • Cite this

    Zwilling, C. E., & Wang, M. Y. (2014). Multivariate voronoi outlier detection for time series. In 2014 IEEE Healthcare Innovation Conference, HIC 2014 (pp. 300-303). [7038934] (2014 IEEE Healthcare Innovation Conference, HIC 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HIC.2014.7038934