Introduction to the theory of neural computation
Introduction to the theory of neural computation
Topology representing networks
Neural Networks
Clustering Algorithms
Self-Organizing Maps
IEEE Transactions on Neural Networks
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
WSEAS Transactions on Information Science and Applications
International Journal of Data Warehousing and Mining
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The measurement of distance is one of the key steps in the unsupervised learning process, as it is through these distance measurements that patterns and correlations are discovered. We examined the characteristics of both non-Euclidean norms and data normalisation within the unsupervised learning environment. We empirically assessed the performance of the K-means, neural gas, growing neural gas and self-organising map algorithms with a range of real-world data sets and concluded that data normalisation is both beneficial in learning class structure and in reducing the unpredictable influence of the norm.