Discovering cluster-based local outliers

Discovering cluster-based local outliers. He, Z., Xu, X., & Deng, S. Pattern Recognition Letters, 24(9):1641–1650, June, 2003.

Paper doi abstract bibtex

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.

@article{he_discovering_2003,
	title = {Discovering cluster-based local outliers},
	volume = {24},
	issn = {0167-8655},
	url = {https://www.sciencedirect.com/science/article/pii/S0167865503000035},
	doi = {10.1016/S0167-8655(03)00003-5},
	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.},
	language = {en},
	number = {9},
	urldate = {2021-08-07},
	journal = {Pattern Recognition Letters},
	author = {He, Zengyou and Xu, Xiaofei and Deng, Shengchun},
	month = jun,
	year = {2003},
	keywords = {Clustering, Data mining, Outlier detection},
	pages = {1641--1650},
}

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