Resolution-based outlier factor: detecting the top-n most outlying data points in engineering data

Author:

Fan Hongqin,Zaïane Osmar R.,Foss Andrew,Wu Junfeng

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software

Reference24 articles.

1. Breunig M, Kriegel H, Ng R, Sander J (2000) LOF: Identifying density-based local outliers. In: Proceedings of ACM SIGMOD international conference on management of data, Dallas

2. Ester M, Kriegel H, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of conference on knowledge discovery and data mining (KDD), Portland, Oregon, USA

3. Fisher D, Xu L, Carmes JR, Chen J, Shiavi R, Biswas G, Weinberg J (1993) Applying AI clustering to engineering tasks. IEEE Intell Syst 8(6): 51–60

4. Foss A, Zaïane O (2002) A parameterless method for efficiently discovering clusters of arbitrary shape in large datasets. In: Proceedings of 2002 IEEE international conference on data mining (ICDM’02), Maebashi City, Japan

5. Gnanadesikan R, Kettenring JR (1972) Robust estimates, residuals, and outlier detection with multi-response data. Biomet J Int Biomet Soc 28: 81–124

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