Inference of number of prototypes with a framework approach to K-means clustering
Simon J. Chambers, Ian H. Jarman, Terence A. Etchells, Paulo Lisböa · International Journal of Biomedical Engineering and Technology · 2013
The selection of an appropriate value of the number of prototypes, K, is an important component in the use of partitioning algorithms such as K-means where such selection is not automatic. This is partly because the purpose of the algorithm is to identify clusters of interest and also because the choice of K is important for ensuring that the resulting partition reflects the underlying structure of the data. This paper introduces a method for guiding the identification of the number of clusters, K, by building upon a larger framework for stabilising partitions using cluster separation and stability. The method is compared with several frequently used algorithms in the published literature, demonstrating the utility of the proposed approach.