Unsupervised clustering with growing self-organizing neural network - a comparison with non-neural approach
Martin Hynar, Michal Burda, Jana Šarmanová · DATESO · 2005
Usually used approaches for non-hierarchical clustering of data are well known k-means or k-medoids methods. However, these fundamental methods are poorly applicable in situations where number of clusters is almost unpredictable. Formerly, they were adapted to al- low splitting and merging when some defined criterion is met. On the other hand there are also methods based on artificial neural networks concretely on self-organizing maps. One of the interesting ideas in this domain is to allow growing of the net which corresponds to adapted k- means method. In this article we are going to compare both approaches in a view of ability to detect clusters in unknown data.