Scalable dynamic self-organising maps for mining massive textual data
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Manufacturing yield improvement by clustering
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Semi-supervised learning of dynamic self-organising maps
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
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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.