Allinson, N. M. and Yin, Hujun (1998) Interactive and semantic data visualisation using selforganising maps. In: IEE Colloquium on Neural Networks in Interactive Multimedia Systems (Ref No 1998/446), 22 October 1998, London.
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Abstract
A review of recent development of the selforganising map (SOM) for applications related to data mapping and visualisation is presented. Neural networks are biological inspired learning and mapping methods, which can learn complex nonlinear relationships between variables from supplied data samples. The algorithm is being exploited as a useful and increasingly promising mapping method for structure visualisation, data mining, knowledge discovery and retrieval. The SOM is a simple mathematical model of the mappings which exist in parts of the mammalian cortex (especially visual and auditory cortex). The inputs to a SOM are often drawn from high dimensional space and the algorithm has been used, in an innovative approach, as visualisation tool for dimensionality reduction projections. One of the most important properties of the SOM is its topological preservation, i.e. neighbouring points in the input space will be mapped to nearby neuron nodes in the map space. Such a property can be employed for the visualisation of mutual semantic relationships between inputs. The SOM is also a reducing process and can approximate a vast quantity of data in a much diminished set of representatives
Item Type:  Conference or Workshop contribution (Paper) 

Additional Information:  A review of recent development of the selforganising map (SOM) for applications related to data mapping and visualisation is presented. Neural networks are biological inspired learning and mapping methods, which can learn complex nonlinear relationships between variables from supplied data samples. The algorithm is being exploited as a useful and increasingly promising mapping method for structure visualisation, data mining, knowledge discovery and retrieval. The SOM is a simple mathematical model of the mappings which exist in parts of the mammalian cortex (especially visual and auditory cortex). The inputs to a SOM are often drawn from high dimensional space and the algorithm has been used, in an innovative approach, as visualisation tool for dimensionality reduction projections. One of the most important properties of the SOM is its topological preservation, i.e. neighbouring points in the input space will be mapped to nearby neuron nodes in the map space. Such a property can be employed for the visualisation of mutual semantic relationships between inputs. The SOM is also a reducing process and can approximate a vast quantity of data in a much diminished set of representatives 
Keywords:  data visualisation, selforganising maps, semantic data visualisation, structure visualisation, data mining, data mapping 
Subjects:  G Mathematical and Computer Sciences > G730 Neural Computing 
Divisions:  College of Science > School of Computer Science 
ID Code:  5091 
Deposited By:  Tammie Farley 
Deposited On:  23 Apr 2012 11:50 
Last Modified:  13 Mar 2013 09:06 
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