Self-organising maps use for intelligent data analysis
A neural-network-based data-analysis model for the prediction and classification of field data has many attractions. However, there are problems in ensuring the generalisation capability of the data analysis model, in measuring the similarity between the original training data and the new unknown data, and in processing large data volumes. This paper proposes the use of self-organising maps (SOMs) to overcome these difficulties and illustrates the utility of the approach though applications in the agricultural, resource exploration and mineral processing areas. In most SOM applications, its self-organising and clustering capabilities have always been the focus. In this paper, SOM is used as enhancement approach that can be incorporated within another intelligent data analysis approach.
|Publication Type:||Journal Article|
|Murdoch Affiliation:||School of Information Technology|
|Publisher:||Australian National University|
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