Abstract
We are interested in the problem of outlier detection, which is the discovery of data that deviate a lot from other data patterns. Hawkins [7] characterizes an outlier in a quite intuitive way as follows: An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism.
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Chen, Z., Fu, A.WC., Tang, J. (2003). On Complementarity of Cluster and Outlier Detection Schemes. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2003. Lecture Notes in Computer Science, vol 2737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45228-7_24
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DOI: https://doi.org/10.1007/978-3-540-45228-7_24
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