DocumentCode :
3440747
Title :
Outlier detection using k-nearest neighbour graph
Author :
Hautamäki, Ville ; Kärkkäinen, Ismo ; Fränti, Pasi
Author_Institution :
Dept. of Comput. Sci., Joensuu Univ., Finland
Volume :
3
fYear :
2004
fDate :
23-26 Aug. 2004
Firstpage :
430
Abstract :
We present an outlier detection using indegree number (ODIN) algorithm that utilizes k-nearest neighbour graph. Improvements to existing kNN distance-based method are also proposed. We compare the methods with real and synthetic datasets. The results show that the proposed method achieves reasonable results with synthetic data and outperforms compared methods with real data sets with small number of observations.
Keywords :
graph theory; pattern clustering; distance based method; indegree number algorithm; k-nearest neighbour graph; outlier detection algorithm; Breast cancer; Cancer detection; Computer science; Computer security; Data mining; Gaussian distribution; Intrusion detection; Pattern recognition; Probability density function; Statistical distributions;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on
ISSN :
1051-4651
Print_ISBN :
0-7695-2128-2
Type :
conf
DOI :
10.1109/ICPR.2004.1334558
Filename :
1334558
Link To Document :
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