An Empirical Comparison of Outlier Detection Algorithms

In recent years, researchers have proposed many different techniques for detecting outliers and other anomalies in data sets. In this paper we wish to examine a subset of these techniques, those that have been designed to discover outliers quickly. The algorithms in question are ORCA, LOADED, and RE...

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Main Authors: Matthew Eric Otey, Srinivasan Parthasarathy, Amol Ghoting
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Subjects:
Orca
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.7648
http://www.cse.ohio-state.edu/dmrl/papers/kddws05.pdf
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spelling ftciteseerx:oai:CiteSeerX.psu:10.1.1.136.7648 2023-05-15T17:53:31+02:00 An Empirical Comparison of Outlier Detection Algorithms Matthew Eric Otey Srinivasan Parthasarathy Amol Ghoting The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.7648 http://www.cse.ohio-state.edu/dmrl/papers/kddws05.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.7648 http://www.cse.ohio-state.edu/dmrl/papers/kddws05.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.cse.ohio-state.edu/dmrl/papers/kddws05.pdf text ftciteseerx 2016-01-07T14:44:47Z In recent years, researchers have proposed many different techniques for detecting outliers and other anomalies in data sets. In this paper we wish to examine a subset of these techniques, those that have been designed to discover outliers quickly. The algorithms in question are ORCA, LOADED, and RELOADED. We have performed an empirical evaluation of these algorithms, and here present our results as guide to their strengths and weaknesses. 1. Text Orca Unknown
institution Open Polar
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op_collection_id ftciteseerx
language English
description In recent years, researchers have proposed many different techniques for detecting outliers and other anomalies in data sets. In this paper we wish to examine a subset of these techniques, those that have been designed to discover outliers quickly. The algorithms in question are ORCA, LOADED, and RELOADED. We have performed an empirical evaluation of these algorithms, and here present our results as guide to their strengths and weaknesses. 1.
author2 The Pennsylvania State University CiteSeerX Archives
format Text
author Matthew Eric Otey
Srinivasan Parthasarathy
Amol Ghoting
spellingShingle Matthew Eric Otey
Srinivasan Parthasarathy
Amol Ghoting
An Empirical Comparison of Outlier Detection Algorithms
author_facet Matthew Eric Otey
Srinivasan Parthasarathy
Amol Ghoting
author_sort Matthew Eric Otey
title An Empirical Comparison of Outlier Detection Algorithms
title_short An Empirical Comparison of Outlier Detection Algorithms
title_full An Empirical Comparison of Outlier Detection Algorithms
title_fullStr An Empirical Comparison of Outlier Detection Algorithms
title_full_unstemmed An Empirical Comparison of Outlier Detection Algorithms
title_sort empirical comparison of outlier detection algorithms
url http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.7648
http://www.cse.ohio-state.edu/dmrl/papers/kddws05.pdf
genre Orca
genre_facet Orca
op_source http://www.cse.ohio-state.edu/dmrl/papers/kddws05.pdf
op_relation http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.7648
http://www.cse.ohio-state.edu/dmrl/papers/kddws05.pdf
op_rights Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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