The Research Of Clustering Visualization Methods Based On SOM Neural Networks

With the wide application of databases and sharp development of Internet, the capacity of utilizing information technology to manufacture and collect data has improved greatly. It is an urgent problem to mine useful information or knowledge from large databases or data warehouses. Therefore, data mining technology is developed rapidly to meet the need. But data mining(DM) often faces so much data which is noisy, disorder and nonlinear. Fortunately, artificial neural network(ANN) is suitable to solve the before-mentioned problems of DM because ANN has such merits as good robustness, adaptability, parallel-disposal, distributing-memory and high fault tolerance.Cluster analysis is an important function in the data mining techniques; especially it has superiority for highter dimensional data. the self-organizing map(SOM) neural network is a excellent tool for datamining, machine learning, pattern classification and visualization. And the visualization is important method for data mining in bioinformatics.As clustering algorithm, the disseration summarizes and analyzes some existing data visualization techniques, and in detail studies results of clustering analysis. an approach of nonlinear principal component analysis(NLPCA)and self-organizing map(SOM) neural network is presented in this paper, which can discuss clustering and visualization of gene expression data. The experiment results shows that the performance of clustering gene expression data based on the SOM network is efficient.