Multivariate voronoi outlier detection for time series

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.

Covariance based outlier detection with feature selection

“…1, extends our multivariate Voronoi outlier detection approach [5] through a powerful feature selection procedure. Each of these steps is now discussed in more detail.…”

“Want to come play with me?” Outlier subgroup discovery on spatio‐temporal interactions

“…Moreover, Wang et al (Zwilling & Wang 2014) propose Multivariate Voronoi Outlier Detection for outlier detection in multivariate time series through Voronoi diagrams which plays an important role in healthcare delivery and management domains.…”

“…Qu (2008) suggest the use of Voronoi diagrams to measure the outlierness and avoid the parameter k. The authors define a Voronoi diagram as a subdivision of the objects into Voronoi cells. The Voronoi cell, V(o) for an object o, is composed of the set of points s in the space that are closer to o than to any other object o 0 D\{o}: Zwilling and Wang (2014) propose Multivariate Voronoi Outlier Detection for outlier detection in multivariate time series through Voronoi diagrams which plays an important role in healthcare delivery and management domains.…”

Multivariate Computing and Robust Estimating for Outlier and Novelty in Data and Imaging Sciences

“…In this chapter, we review and also propose one type of data mining and pattern recognition strategy that has been under development in multiple disciplines (e.g. statistics and machine learning) with important applications ----outlier or novelty detection [1][2][3][4].…”