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.
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© 2006 Springer-Verlag Berlin Heidelberg
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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: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_66
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DOI: https://doi.org/10.1007/11731139_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33206-0
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