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11366 A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets
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Zhang, Yang and Meratnia, N. and Havinga, P.J.M. (2007) A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets. Technical Report TR-CTIT-07-79, Centre for Telematics and Information Technology University of Twente, Enschede. ISSN 1381-3625

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Abstract

The term "outlier" can generally be defined as an observation that is significantly different from
the other values in a data set. The outliers may be instances of error or indicate events. The
task of outlier detection aims at identifying such outliers in order to improve the analysis of
data and further discover interesting and useful knowledge about unusual events within numerous
applications domains. In this paper, we report on contemporary unsupervised outlier detection
techniques for multiple types of data sets and provide a comprehensive taxonomy framework and
two decision trees to select the most suitable technique based on data set. Furthermore, we
highlight the advantages, disadvantages and performance issues of each class of outlier detection
techniques under this taxonomy framework.

Item Type:Internal Report (Technical Report)
Research Group:EWI-CAES: Computer Architecture for Embedded Systems
Research Program:CTIT-WiSe: Wireless and Sensor Systems
Research Project:e-SENSE: Capturing Ambient Intelligence for Mobile Communications through Wireless Sensor Networks
ID Code:11366
Deposited On:20 November 2007
More Information:statisticsmetis

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