A self-organizing semantic map for information retrieval
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Self-organizing maps
Pattern recognition in soft computing paradigm
Self-Organizing neural networks
Comparing Self-Organizing Maps
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
A nonlinear projection method based on Kohonen's topology preserving maps
IEEE Transactions on Neural Networks
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The Self Organising Map (SOM) is an artificial neural network technique that has been successfully applied in clustering and visualisation tasks in data mining. In this paper, we propose a new SOM-based visualisation, Neural Unit Shape Representation. Its advantage over the previous SOM-based visualisation, U-matrix and Component Planes, is that it gives visual cues to the properties of individual neural units or nodes on the map, enabling direct examination of the produced ordering. Further, the combination of the Neural Unit Shape Representation and a new way of drawing boundaries using U-matrix gives a good visualisation and helps people, even without prior knowledge of data, to find interesting patterns.