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Growing self-organizing map for online continuous clustering

Smith, Toby and Alahakoon, Damminda 2009, Growing self-organizing map for online continuous clustering. In Abraham, Ajith, Hassanien, Aboul-Ella and de, Carvalho Andre Ponce de Leon F. (ed), Foundations of computational intelligence volume 4, Springer-Verlag, Berlin, Germany, pp.49-83, doi: 10.1007/978-3-642-01088-0_3.

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Title Growing self-organizing map for online continuous clustering
Author(s) Smith, Toby
Alahakoon, Damminda
Title of book Foundations of computational intelligence volume 4
Editor(s) Abraham, Ajith
Hassanien, Aboul-Ella
de, Carvalho Andre Ponce de Leon F.
Publication date 2009
Series Studies in Computational Intelligence
Chapter number 3
Total chapters 16
Start page 49
End page 83
Total pages 35
Publisher Springer-Verlag
Place of Publication Berlin, Germany
Summary 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.
ISBN 3642010881
Language eng
DOI 10.1007/978-3-642-01088-0_3
Field of Research 109999 Technology not elsewhere classified
Socio Economic Objective 970110 Expanding Knowledge in Technology
HERDC Research category B1.1 Book chapter
Copyright notice ©2009, Springer
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Document type: Book Chapter
Collection: School of Information and Business Analytics
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