Analysis of DNA microarray data using self-organizing map and kernel based clustering

We describe a method of combining a self-organizing map (SOM) and a kernel based clustering for analyzing and categorizing the gene expression data obtained from DNA microarray. The SOM is an unsupervised neural network learning algorithm and forms a mapping a high-dimensional data to a two-dimensional space. However, it is difficult to find clustering boundaries from results of the SOM. On the other hand, the kernel based clustering can partition the data nonlinearly. In order to understand the results of SOM easily, we apply the kernel based clustering to finding the clustering boundaries and show that the proposed method is effective for categorizing the gene expression data.