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A Survey on Outlier Detection in the Context of Stream Mining: Review of Existing Approaches and Recommadations

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Intelligent Systems Design and Applications (ISDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

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Abstract

Generally, extracting only expected knowledge from data is not sufficient since unexpected ones can hide useful information concerning the data behavior. These information can be further used to optimize the current state. This has lead to the outlier detection. It refers to the data mining task that aims to find abnormal points or sequence of data hidden in the dataset. In fact, due to the emergence of new technologies, applications often generate and consume data in form of streams. This data differs from the static one. Therefore, traditional techniques cannot be used. Hence, convenient ones suitable to the data stream nature must be applied. In this paper, we will review different techniques of outlier detection in the data streams. In addition, we shall describe different approaches based on these techniques in order to establish a comparative study based on different criterion. This study aims to help users and facilitates the choice of the appropriate algorithm for a certain context.

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Author information

Authors and Affiliations

  1. Higher Institute of Computer Science and Management, Kairouan, Tunisia

    Imen Souiden

  2. Higher Institute of Computer Science and Communication Techniques, Hammam Sousse, Tunisia

    Zaki Brahmi

  3. RIADI-GDL Lab, Manouba University, Manouba, Tunisia

    Zaki Brahmi & Hajer Toumi

Authors
  1. Imen Souiden
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  2. Zaki Brahmi
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  3. Hajer Toumi
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Corresponding author

Correspondence to Imen Souiden .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

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Souiden, I., Brahmi, Z., Toumi, H. (2017). A Survey on Outlier Detection in the Context of Stream Mining: Review of Existing Approaches and Recommadations. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_37

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  • DOI: https://doi.org/10.1007/978-3-319-53480-0_37

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  • Online ISBN: 978-3-319-53480-0

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