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Title:
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STATE OF THE ART OF OUTLIER DETECTION IN STREAMING DATA |
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Author(s):
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Amardeep Kaur , M.p.s. Bhatia , S.m. Bhaskar |
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ISBN:
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978-972-8924-40-9 |
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Editors:
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Jörg Roth, Jairo Gutiérrez and Ajith P. Abraham (series editors: Piet Kommers, Pedro Isaías and Nian-Shing Chen) |
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Year:
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2007 |
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Edition:
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Single |
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Keywords:
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Data Stream Mining, Outlier Detection. |
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Type:
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Reflection Paper |
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First Page:
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209 |
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Last Page:
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212 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Nowadays a growing number of applications like Internet, stock markets and sensors generate streams of data. A data
stream is a massive sequence of data elements continuously generated at a rapid rate. The identification of outliers in the
streaming data is an important research area because of its numerous applications, including network intrusion detection,
fraud detection, and surveillance. In this paper we present state-of-the-art of various techniques being used for outlier
detection in streaming data. |
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