基于PCA的仿射传播聚类算法

仿射传播聚类是一种快速有效的聚类方法。但对高维数据进行聚类时,由于数据信息的重叠,聚类结果往往会有较大误差。针对这个问题,提出了把主元分析(PCA)和仿射传播(AP)聚类相结合的PCA-AP算法,在保留原变量绝大部分信息的情况下对数据进行降维处理,然后在低维空间中用仿射传播聚类的方法进行聚类。由于剔除了冗余信息,算法得到的分类结果更加准确。实验结果表明该算法是有效的。

Affinity propagation clustering algorithm based on principal components analysis

Affinity propagation clustering is a fast and efficient clustering algorithm.However,because of the overlap of the data information,error of clustering is biggish when it is applied to high-dimensional data.Concerning this problem,a new method combining Principal Components Analysis(PCA) and Affinity Propagation(AP) clustering is proposed.In this method, dimensionality of the original data is reduced on the premise of reserving most information of the variables.Then,AP clustering is implemented in the low-d...