A Nonparametric Outlier Detection for Effectively Discovering Top-N outliers from Engineering Data (2006)
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'06)
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. [via]
http://www.cs.ualberta.ca/~zaiane/postscript/...
engineering
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