人工免疫投影寻踪降维模型——AI-PPC

引入人工免疫(AI)系统原理用于解决投影寻踪(PP)降维问题，利用免疫克隆选择算法优化投影方向，将高维的特征数据投影到低维空间上，从而降低了数据挖掘过程中的计算复杂度，实现了数据的约减；并用K-Means等聚类算法分别对初始数据和降维后的数据进行聚类对比。实验结果验证了人工免疫投影寻踪降维(AI-PPC)算法的有效性。

AI-PPC: Projection pursuit model for dimension reduction based on artificial immune algorithm

To solve the dimension reduction with Projection Pursuit (PP) model, the theory of Artificial Immune (AI) system was introduced. The immune clone-selecting algorithm was used to optimize the projecting direction, with the purpose to project the data from high dimensional space to a low one. Therefore, the projection not only reduced the computation complexity during the process of data mining, but also made the data shrinking possible. Besides, the clustering results between the initial and the processed data with K-Means and other clustering algorithms were compared. And the experimental results verify the validity of the proposed algorithm.