Feature Data Enriching Approach Based on Immune Clustering

Aiming at the relativity between repeated or similar samples and characteristic parameters during diagnosis of characteristic data,an effective data analysis approach for characteristic data compression from bi direction is presented,which can reduce the burden of learning machine without losing the connotative characteristic knowledge of characteristic data.At the first step of the algorithm,based on the theory of principal component analysis,a modified principal component analysis(MPCA)approach is adopted to reduce the dimension of data horizontally,then after comparing existing clustering algorithms,an immune clustering algorithm is put forward based on similarity measurement of principle component core for vertical reduction by using related mechanism of clone selection as well as immune network self stabilization in natural organic immune system for reference.Finally,its effectiveness is proved by the simulation experiments.