Abstract
“An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism.”
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Note that higher \(k\)-nearest neighbor distances indicate greater outlierness.
- 2.
We say “almost,” because the very last distance computation for \(\overline {X}\) may bring \(V(\overline {X})\) below \(L\). This scenario is unusual, but might occasionally occur.
- 3.
Most descriptions in the literature omit the first phase of sampling, which is very important for efficiency maximization. A number of implementations in time-series analysis [306] do order the data points more carefully but not with sampling.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Aggarwal, C. (2015). Outlier Analysis. In: Data Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-14142-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-14142-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14141-1
Online ISBN: 978-3-319-14142-8
eBook Packages: Computer ScienceComputer Science (R0)