Publication - Variable Sensitivity in Unsupervised Clustering Tasks with an n-tuple-based Self-Organising Neural Network

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Variable Sensitivity in Unsupervised Clustering Tasks with an n-tuple-based Self-Organising Neural Network

Research Area:  
Other topics in Computer Science
    
Type:  
Journal article

 

Year: 2000
Authors: George Tambouratzis
Journal: International Journal of Neural Systems
Volume: 10
Number: 2
Pages: 107-121
DOI: 10.1142/S0129065700000107
Abstract:
This article investigates the application of the SOLNN (Self-Organising Logic Neural Network) n-tuple-based network to character recognition and image segmentation clustering tasks, where the classes consist of a large number of distinct sub-classes. It is shown that the SOLNN clustering performance and node utilisation are both improved by virtue of the distribution constraint mechanism. The clustering results are supported by means of a detailed analysis of the characteristics of each pattern space. This analysis, coupled with comparative results obtained using other self-organising models, illustrates that the SOLNN clusters the patterns in accordance to the pattern space characteristics and thus is well-suited to clustering complex datasets.
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