Discovering cluster-based local outliers

作者：He ZY^*; Xu XF; Deng SC

来源：Pattern Recognition Letters, 2003, 24(9-10): 1641-1650.

DOI：10.1016/S0167-8655(03)00003-5

• outlier detection
• clustering
• data mining

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.

• 出版日期2003-6
• 单位哈尔滨工业大学