Cluster analysis using self-organizing maps and image processing techniques

Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into groups based on their similarities. This paper focuses on the use of self organising maps (SOM) as a clustering tool and some of the additional procedures required to enable a meaningful cluster's interpretation in the trained map. Topics discussed here include the usage of mathematical morphology segmentation method watershed to segment the neuron's distance image (u-matrix). Finding good watershed markers and the modification of the u-matrix homotopy are discussed. The algorithm automatically produces labeled sets of neurons that are related to the clusters in the P-dimensional space. An example of non-spherical, complex shaped and nonlinearly separable clusters illustrate the capabilities of the method.