Aiming at the massive gene expression data brought by gene chip technology

in order to fully mine the biological information and potential biological mechanisms contained in it

this paper proposes a gene clustering algorithm based on CCA- hierarchical clustering ( CCA-Hc) . The algorithm introduces canonical correlation analysis on the basis of hierarchical clustering

and optimizes the calculation method of similarity matrix. First

the canonical correlation analysis method is used to measure the gene correlation by combining the multiple feature information of the gene

and the gene similarity matrix is obtained. Then the similarity matrix is used as the neighbor matrix of hierarchical clustering for agglomerative hierarchical clustering. The CCA-Hc clustering effect test experiment was performed on the gene expression dataset of Oryza sativa L. ( rice ) . The results show that

compared with the traditional hierarchical clustering algorithm using Euclidean distance ( EUC-Hc )

CCA-Hc is superior to EUC-Hc in both internal stability index and biological functional index

and has better robustness and clustering accuracy. It is more conducive to discovering the co-expression relationship between genes.