Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Analysing the similarity of album art with self-organising maps
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Self-Organizing Map Formation with a Selectively Refractory Neighborhood
Neural Processing Letters
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SOMs have proven to be a very powerful tool for data analysis. However, comparing multiple SOMs trained on the same data set using different parameters or initialisations is still a difficult task. In most cases it is performed only via visual inspection or by utilising one of a range of quality measures to compare vector quantisation or topology preservation characteristics of the maps. Yet, comparing SOMs systematically is both necessary as well as a powerful tool to further analyse data: necessary, because it may help to pick the most suitable SOM out of different training runs; a powerful tool because it allows analysing mapping stabilities across a range of parameter settings. In this paper we present an analytic approach to compare multiple SOMs trained on the same data set. Analysis of output space mapping, supported by a set of visualisations, reveals data co-locations and shifts on pairs of SOMs, considering both different neighbourhood sizes at source and target maps. A similar concept of mutual distances and relationships can be analysed at a cluster level. Finally, Comparisons aggregated automatically across several SOMs are strong indicators for strength and stability of mappings.