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2007

Unsupervised learning with normalised data and non-Euclidean norms

9 years 12 months ago
Unsupervised learning with normalised data and non-Euclidean norms
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. Key words: Distance Measures, Data Normalisation, Unsupervised Learning, Neural Gas, Growing Neural Gas, Self-Organising Map, K-means
Kevin Doherty, Rod Adams, Neil Davey
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2007
Where ASC
Authors Kevin Doherty, Rod Adams, Neil Davey
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