Comparison of self-organizing map with K-means hierarchical clustering for bioinformatics applications

The self-organizing map (SOM) has emerged as one of the popular choices for clustering data; however, when it comes to point density accuracy of codebooks or reliability and interpretability of the map, the SOM leaves much to be desired. In this paper, we compare the newly developed K-means hierarchical (KMH) clustering algorithm to the SOM. We also introduce a new initialization scheme for the K-means that improves codebook placement and, propose a novel visualization scheme that combines the principal component analysis (PCA) and minimal spanning tree (MST) in an arrangement that ensures reliability of the visualization unlike the SOM. A practical application of the algorithm is demonstrated on a challenging bioinformatics problem.