Variable sensitivity in unsupervised clustering tasks with an n-tuple-based self-organising neural network.
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.
Department of Machine Translation, Institute for Language and Speech, Processing, Athens, Greece.
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Tambouratzis
D000465:Algorithms; D001185:Artificial Intelligence; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012680:Sensitivity and Specificity