摘要
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。
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.
出处
《计算机应用》
CSCD
北大核心
2010年第9期2290-2293,共4页
journal of Computer Applications
基金
福建省省属高校科研专项重点项目(JK2009006)
福建省高校服务海西建设重点项目
关键词
人工免疫
投影寻踪
降维
优化
聚类
Artificial Immunity （ AI）
Projection Pursuit （ PP）
dimension reduction
optimization
clustering
