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Cluster analysis / Machine learning / Robust statistics / Outlier / Anomaly detection / Consensus clustering / Mixture model / Ensemble learning / Statistical ensemble / Statistics / Data analysis / Data mining


Ensembles for Unsupervised Outlier Detection: Challenges and Research Questions [Position Paper] Arthur Zimek Ricardo J. G. B. Campello
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Document Date: 2014-01-31 16:18:34

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File Size: 259,77 KB

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City

Dallas / Pasadena / Helsinki / Austin / Newport Beach / Washington / DC / Philadelphia / Prague / Guildford / Cagliari / Bari / Chicago / /

Company

John Wiley&Sons / Multiple Classifier Systems / Pearson / University of Alberta Edmonton AB / /

Country

Italy / United States / Canada / United Kingdom / China / Singapore / Finland / Czech Republic / /

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Facility

University of S˜ / /

IndustryTerm

outlier detection algorithms / principled solutions / unsupervised data mining task / web server traffic / traffic network / valid solutions / detection algorithms / individual solutions / /

Organization

Condorcet’s Jury / University of S˜ / Pattern Analysis and Machine Intelligence / University of Alberta Edmonton / /

Person

Arthur Zimek Ricardo / /

Position

one of the ensemble members / /

ProgrammingLanguage

DC / /

ProvinceOrState

Texas / Illinois / Pennsylvania / California / /

PublishedMedium

IEEE Transactions on Pattern Analysis and Machine Intelligence / Machine Learning / the Silhouette / /

Technology

machine learning / SIGKDD Explorations Outlier detection algorithms / data mining / outlier detection algorithms / web server / /

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http /

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