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

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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. © Springer-Verlag Berlin Heidelberg 2006.

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Fan, H., Zaïane, O. R., Foss, A., & Wu, J. (2006). A nonparametric outlier detection for effectively discovering top-N outliers from engineering data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3918 LNAI, pp. 557–566). Springer Verlag. https://doi.org/10.1007/11731139_66

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