Abstract: 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.
Publication Year: 2003
Publication Date: 2003-03-26
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 881
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot