基于流形距离的段混合聚类算法研究

聚类分析是一种数据缩减技术,即基于数据特征的相似性将数据聚集成不同的类,是数据挖掘中一种非常有效的工具,得到了人们广泛的关注。从聚类算法中的相似性度量问题入手,采用基于流形距离的相似性度量替代传统的基于欧氏距离的相似性度量,通过二阶段聚类解决引入流形距离带来的计算量增大问题,并将这种聚类算法应用到聚类分析当中。

The Study of Mixture Clustering Algorithm based on Manifold Distance

Cluster analysis is a data reduction technique that groups together either variables or cases based on similar data characteristics. It is an effective tool of data mining and is attracting wide attention. Commencing with the dissimilarity measure, we substitute a novel manifold dissimilarity measure for the traditional Euclidean distance. And this Two-Phase Clustering scheme is proposed to solve the increasing computational cost brought by manifold distance. Then, the new method is applied to cluster analysis.