Discovering cluster-based local outliers
Z He, X Xu, S Deng - Pattern recognition letters, 2003 - Elsevier
Z He, X Xu, S Deng
Pattern recognition letters, 2003•ElsevierIn this paper, we present a new definition for outlier: cluster-based local outlier, which is
meaningful and provides importance to the local data behavior. A measure for identifying the
physical significance of an outlier is designed, which is called cluster-based local outlier
factor (CBLOF). We also propose the FindCBLOF algorithm for discovering outliers. The
experimental results show that our approach outperformed the existing methods on
identifying meaningful and interesting outliers.
meaningful and provides importance to the local data behavior. A measure for identifying the
physical significance of an outlier is designed, which is called cluster-based local outlier
factor (CBLOF). We also propose the FindCBLOF algorithm for discovering outliers. The
experimental results show that our approach outperformed the existing methods on
identifying meaningful and interesting outliers.
In this paper, we present a new definition for outlier: cluster-based local outlier, which is meaningful and provides importance to the local data behavior. A measure for identifying the physical significance of an outlier is designed, which is called cluster-based local outlier factor (CBLOF). We also propose the FindCBLOF algorithm for discovering outliers. The experimental results show that our approach outperformed the existing methods on identifying meaningful and interesting outliers.
Elsevier
