A Nonparametric Outlier Detection for Effectively Discovering Top-N outliers from Engineering Data
- Hongqin Fan
- Osmar R. Zaiane, University of Alberta (Database)
- Andrew Foss, University of Alberta
- Junfeng Wu
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.
Citation
H. Fan, O. Zaiane, A. Foss, J. Wu. "A Nonparametric Outlier Detection for Effectively Discovering Top-N outliers from Engineering Data". Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Singapore, (ed: Wee Keong Ng, Masaru Kitsuregawa, Jianzhong Li, Kuiyu Chang), pp 557-566, April 2006.| Keywords: | |
| Category: | In Conference |
| Web Links: | Springer |
BibTeX
@incollection{Fan+al:PAKDD06,
author = {Hongqin Fan and Osmar R. Zaiane and Andrew Foss and Junfeng Wu},
title = {A Nonparametric Outlier Detection for Effectively Discovering Top-N
outliers from Engineering Data},
Editor = {Wee Keong Ng, Masaru Kitsuregawa, Jianzhong Li, Kuiyu Chang},
Pages = {557-566},
booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining
(PAKDD)},
year = 2006,
}Last Updated: January 30, 2020Submitted by Sabina P
