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Chapter title |
A Nonparametric Outlier Detection for Effectively Discovering Top-N Outliers from Engineering Data
|
---|---|
Chapter number | 66 |
Book title |
Advances in Knowledge Discovery and Data Mining
|
Published by |
Springer, Berlin, Heidelberg, April 2006
|
DOI | 10.1007/11731139_66 |
Book ISBNs |
978-3-54-033206-0, 978-3-54-033207-7
|
Authors |
Hongqin Fan, Osmar R. Zaïane, Andrew Foss, Junfeng Wu |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Canada | 1 | 3% |
Unknown | 28 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 8 | 28% |
Researcher | 5 | 17% |
Student > Master | 5 | 17% |
Student > Bachelor | 4 | 14% |
Professor | 2 | 7% |
Other | 2 | 7% |
Unknown | 3 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 15 | 52% |
Economics, Econometrics and Finance | 2 | 7% |
Engineering | 2 | 7% |
Agricultural and Biological Sciences | 2 | 7% |
Business, Management and Accounting | 1 | 3% |
Other | 4 | 14% |
Unknown | 3 | 10% |