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Multivariate Voronoi Outlier Detection for Time Series.
Chris E Zwilling1, Michelle Yongmei Wang2
1Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA.
... Health Innovations and Point-Of-Care Technologies Conference. Health Innovations and Point-Of-Care Technologies Conference
|May 19, 2015
Summary
This study introduces Multivariate Voronoi Outlier Detection (MVOD), a novel method for identifying anomalies in complex datasets. MVOD accurately detects outliers in multivariate time series, enhancing data mining and medical research.
Area of Science:
- Data Mining
- Machine Learning
- Time Series Analysis
Background:
- Outlier detection is crucial in data mining and medical research.
- Existing methods may struggle with multivariate time series data.
Purpose of the Study:
- To present a general method for outlier detection in multivariate time series.
- Introduce Multivariate Voronoi Outlier Detection (MVOD).
Main Methods:
- Utilizes Voronoi diagrams to define neighborhood relationships.
- Extracts effective parametric or nonparametric features from multivariate data.
- Applies a multivariate framework for outlier identification.
Main Results:
- MVOD accurately differentiates outliers from non-outliers.
- Demonstrates high sensitivity and robustness in outlier detection.
- Experimental evaluations confirm the method's effectiveness.
Conclusions:
- MVOD provides an accurate and robust approach for outlier detection in multivariate time series.
- The method is applicable to various data mining and healthcare applications.
- Voronoi diagrams offer an effective mechanism for neighborhood analysis.
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