Controlling the spread of dynamic self-organising maps

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.

[1]  Risto Miikkulainen,et al.  Incremental grid growing: encoding high-dimensional structure into a two-dimensional feature map , 1993, IEEE International Conference on Neural Networks.

[2]  Thomas Martinetz,et al.  Topology representing networks , 1994, Neural Networks.

[3]  Saman K. Halgamuge,et al.  A self-growing cluster development approach to data mining , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[4]  Bernd Fritzke,et al.  Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.

[5]  Bala Srinivasan,et al.  Dynamic self-organizing maps with controlled growth for knowledge discovery , 2000, IEEE Trans. Neural Networks Learn. Syst..