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Comparative study of incremental learning algorithms in multidimensional outlier detection on data stream
Department of Computer and Information Science, University of Macau.
Department of Computer and Information Science, University of Macau.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-1902-9877
2015 (English)In: Improving Knowledge Discovery through the Integration of Data Mining Techniques, Hershey, PA: IGI Global, 2015, p. 54-73Chapter in book (Refereed)
Abstract [en]

Multi-dimensional outlier detection (MOD) over data streams is one of the most significant data stream mining techniques. When multivariate data are streaming in high speed, outliers are to be detected efficiently and accurately. Conventional outlier detection method is based on observing the full dataset and its statistical distribution. The data is assumed stationary. However, this conventional method has an inherent limitation-it always assumes the availability of the entire dataset. In modern applications, especially those that operate in the real time environment, the data arrive in the form of live data feed; they are dynamic and ever evolving in terms of their statistical distribution and concepts. Outlier detection should no longer be done in batches, but in incremental manner. In this chapter, we investigate into this important concept of MOD. In particular, we evaluate the effectiveness of a collection of incremental learning algorithms which are the underlying pattern recognition mechanisms for MOD. Specifically, we combine incremental learning algorithms into three types of MOD-Global Analysis, Cumulative Analysis and Lightweight Analysis with Sliding Window. Different classification algorithms are put under test for performance comparison

Place, publisher, year, edition, pages
Hershey, PA: IGI Global, 2015. p. 54-73
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-20961DOI: 10.4018/978-1-4666-8513-0.ch004Scopus ID: 2-s2.0-84957402163Local ID: 9b03a2f7-1b24-4af4-ab1e-4c93fc648355ISBN: 9781466685130 (print)ISBN: 9781466685147 (electronic)OAI: oai:DiVA.org:ltu-20961DiVA, id: diva2:994005
Note
Godkänd; 2015; 20160217 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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Vasilakos, Athanasios

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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