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Clustering Techniques for Outlier Detection
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Author(s): Frank Klawonn (University of Applied Sciences Braunschweig/Wolfenbuettel, Germany)and Frank Rehm (German Aerospace Center, Germany)
Copyright: 2005
Pages: 4
Source title:
Encyclopedia of Data Warehousing and Mining
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
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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