Visualization Methods Based on NLPCA in Cluster
QI Zhi1,LI Ji1,ZHAO Xiao-dan2 ( 1. School of Information Technology,Changchun Vocational Institute of Technology,Changchun 130033,China; 2. School of International Business,Jilin Province Economics and Management Cadres College,Changchun 130012,China)
To project high-dimensional data into low-dimensional space can be effectively utilized to visualize and explore properties of data. An approach of NLPCA ( NonLinear Principal Component Analysis) and SOM ( Self-Organizing Map) neural network is presented for clustering and visualization of gene expression data. The experiment results show that the performance of clustering gene expression data based on the SOM network is efficient.
【CateGory Index】： TP311.13