Unsupervised outlier detection in multidimensional data

Very Fast Outlier Detection in Large Multidimensional Data Sets

Outliers are objects that do not comply with the general behavior of the data. Applications such as exploration in science databases need fast interactive tools for outlier detection in data sets that have unknown distributions, are large in size, and are in high dimensional space. Existing algorithms for outlier detection are too slow for such applications. We present an algorithm based on an ...

Outlier Detection in Multivariate Data

The objective of this research is detection of outliers in multivariate data employing various distance measure, particularly using robust regression diagnosis technique. Several classical outlier identification methods are based on the sample mean and covariance matrix in general. But they do not always yield better result, as they themselves are affected by the outliers. Sometimes one outlier...

Outlier detection in astronomical data

Astronomical data sets have experienced an unprecedented and continuing growth in the volume, quality, and complexity over the past few years, driven by the advances in telescope, detector, and computer technology. Like many other fields, astronomy has become a very data rich science. Information content measured in multiple Terabytes, and even larger, multi Petabyte data sets are on the horizo...

Finding Outlier DMUs in Data Envelopment Analysis

Outlier Detection with Uncertain Data

In recent years, many new techniques have been developed for mining and managing uncertain data. This is because of the new ways of collecting data which has resulted in enormous amounts of inconsistent or missing data. Such data is often remodeled in the form of uncertain data. In this paper, we will examine the problem of outlier detection with uncertain data sets. The outlier detection probl...