A nonparametric outlier detection for effectively discovering top-n outliers from engineering data

Authors:
Hongqin Fan;Osmar R. Zaïane;Andrew Foss;Junfeng Wu
Affiliations:
Department of Civil Engineering, University of Alberta, Canada;Department of Computing Science, University of Alberta, Canada;Department of Computing Science, University of Alberta, Canada;Department of Computing Science, University of Alberta, Canada
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

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Abstract

We present a novel resolution-based outlier notion and a nonparametric outlier-mining algorithm, which can efficiently identify top listed outliers from a wide variety of datasets. The algorithm generates reasonable outlier results by taking both local and global features of a dataset into consideration. Experiments are conducted using both synthetic datasets and a real life construction equipment dataset from a large building contractor. Comparison with the current outlier mining algorithms indicates that the proposed algorithm is more effective.