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Growing Self-Organizing Map for Online Continuous Clustering

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Book cover Foundations of Computational Intelligence Volume 4

Part of the book series: Studies in Computational Intelligence ((SCI,volume 204))

Abstract

The internet age has fuelled an enormous explosion in the amount of information generated by humanity. Much of this information is transient in nature, created to be immediately consumed and built upon (or discarded). The field of data mining is surprisingly scant with algorithms that are geared towards the unsupervised knowledge extraction of such dynamic data streams. This chapter describes a new neural network algorithm inspired by self-organising maps. The new algorithm is a hybrid algorithm from the growing self-organising map (GSOM) and the cellular probabilistic self-organising map (CPSOM). The result is an algorithm which generates a dynamically growing feature map for the purpose of clustering dynamic data streams and tracking clusters as they evolve in the data stream.

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Author information

Authors and Affiliations

  1. Cognitive and Connectionist Systems Lab, Monash University, Australia

    Toby Smith & Damminda Alahakoon

Authors
  1. Toby Smith
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  2. Damminda Alahakoon
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Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Auburn, Washington, USA

    Ajith Abraham

  2. College of Business Administration, Quantitative and Information System Department, Kuwait University, P.O. Box 5486, 13055, Safat, Kuwait

    Aboul-Ella Hassanien

  3. Department of Computer Science, University of São Paulo,SCE - ICMSC - USP, Caixa Postal 668, 13560-970, Sao Carlos, SP, Brazil

    André Ponce de Leon F. de Carvalho

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© 2009 Springer-Verlag Berlin Heidelberg

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Smith, T., Alahakoon, D. (2009). Growing Self-Organizing Map for Online Continuous Clustering. In: Abraham, A., Hassanien, AE., de Carvalho, A.P.d.L.F. (eds) Foundations of Computational Intelligence Volume 4. Studies in Computational Intelligence, vol 204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01088-0_3

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  • DOI: https://doi.org/10.1007/978-3-642-01088-0_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01087-3

  • Online ISBN: 978-3-642-01088-0

  • eBook Packages: EngineeringEngineering (R0)

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