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Clustering Over Data Streams Based on Growing Neural Gas

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Advances in Knowledge Discovery and Data Mining (PAKDD 2015)

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Abstract

Clustering data streams requires a process capable of partitioning observations continuously with restrictions of memory and time. In this paper we present a new algorithm, called G-Stream, for clustering data streams by making one pass over the data. G-Stream is based on growing neural gas, that allows us to discover clusters of arbitrary shape without any assumptions on the number of clusters. By using a reservoir, and applying a fading function, the quality of clustering is improved. The performance of the proposed algorithm is evaluated on public data sets.

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Author information

Authors and Affiliations

  1. University of Paris 13, Sorbonne Paris City LIPN-UMR 7030 - CNRS, 99, av. J-B Clément, 93430, Villetaneuse, France

    Mohammed Ghesmoune, Mustapha Lebbah & Hanene Azzag

Authors
  1. Mohammed Ghesmoune
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  2. Mustapha Lebbah
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  3. Hanene Azzag
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Corresponding author

Correspondence to Mohammed Ghesmoune .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tru Cao

  2. Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  3. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  4. Japan Advanced Institute of Science and Technology, Nomi City, Japan

    Tu-Bao Ho

  5. The University of Hong Kong, Hong Kong, Hong Kong SAR

    David Cheung

  6. Osaka University, Osaka, Japan

    Hiroshi Motoda

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Ghesmoune, M., Lebbah, M., Azzag, H. (2015). Clustering Over Data Streams Based on Growing Neural Gas. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9078. Springer, Cham. https://doi.org/10.1007/978-3-319-18032-8_11

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  • DOI: https://doi.org/10.1007/978-3-319-18032-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18031-1

  • Online ISBN: 978-3-319-18032-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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