Clustering Techniques for Outlier Detection

Clustering Techniques for Outlier Detection

Frank Klawonn (University of Applied Sciences Braunschweig/Wolfenbuettel, Germany) and Frank Rehm (German Aerospace Center, Germany)
Copyright: © 2005 |Pages: 4
DOI: 10.4018/978-1-59140-557-3.ch035
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Abstract

For many applications in knowledge discovery in databases, finding outliers, which are rare events, is of importance. Outliers are observations that deviate significantly from the rest of the data, so they seem to have been generated by another process (Hawkins, 1980). Such outlier objects often contain information about an untypical behaviour of the system.

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