A self-organizing maps algorithm for gene expression data clustering based on feature´s distribution

In order to solve the problem that traditional SOM algorithm simply regards all the features as equal importance, a novel similarity computation method is proposed in this paper. This method uses feature´s intra-cluster distribution and inter-cluster distribution to evaluate different features with different weights, and integrate features´ weights in similarity computation. Experiment results demonstrate that this novel similarity computation method can effectively improve precision on gene expression data clustering.