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Controlling the spread of dynamic self-organising maps

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Abstract

The growing self-organising map (GSOM) has recently been proposed as an alternative neural network architecture based on the traditional self-organising map (SOM). The GSOM provides the user with the ability to control the spread of the map by defining a parameter called the spread factor (SF), which results in enhanced data mining and hierarchical clustering opportunities. When experimenting with the SOM, the grid size (number of rows and columns of nodes) can be changed until a suitable cluster distribution is achieved. In this paper we highlight the effect of the spread factor on the GSOM and contrast this effect with grid size change (increase and decrease) in the SOM. We also present experimental results in support of our claims regarding differences between GSOM and SOM.

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Acknowledgements

The author acknowledges that Figs. 4 and 5 were obtained using the GSOM program developed by Mr. Arthur Hsu, University of Melbourne, Australia.

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Authors and Affiliations

  1. School of Business Systems, Monash University, Clayton, Victoria, Australia

    L. D. Alahakoon

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  1. L. D. Alahakoon
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Correspondence to L. D. Alahakoon.

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Alahakoon, L.D. Controlling the spread of dynamic self-organising maps. Neural Comput & Applic 13, 168–174 (2004). https://doi.org/10.1007/s00521-004-0419-y

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  • DOI: https://doi.org/10.1007/s00521-004-0419-y

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