Outlier Detection Based On
Projection-based Ordering
Author(s)
A. Shojaie & P.-N. Tan
Abstract
Simple one-dimensional order statistics have played an important role in
one-dimensional data analysis. However, extensions of these methods to high
dimensional data have not been properly addressed. Even though ordering is of
natural interest in many problems, the lack of natural and unambiguous methods
to order or rank multi-dimensional data has hindered the adoption of these
methods for more complex applications.
Projection-based depth functions are mainly based on the idea of finding a
\“centre-outward” ordering from the \“deepest” point of data. These functions are
providing promising tools for ordering multi-dimensional data and may also be
used for outlier detection and cluster analysis.
This paper develops an outlier-detection algorithm based on a new method for
projection-based ordering of multi-dimensional data.
Keywords: outlier detection, projection-based ordering, data mining.
1 Introduction
Outliers are data points whose characteristics are very different form the rest of
the data based on some measures [1]. There are two potential benefits of
applying outlier detection techniques: (1) for data pre-processing and (2) for
anomaly detection.
Many data mining methods and statistical measures are susceptible to
presence of outliers. It is therefore crucial to detect and eliminate outliers in the
pre-processing step. Mean is an example of a statistical measure that may not
effectively represent the location of data points when outliers are present. Many
Keywords
outlier detection, projection-based ordering, data mining.
Related Book
Other papers in this volume
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