Two-Phase Clustering Algorithm for Complex Distributed Data

Abstract

提出了一种用于复杂分布数据的二阶段聚类算法(two-phase clustering,简称TPC),TPC 包含两个阶段:首先将数据划分为若干个球形分布的子类,每一个子类用其聚类中心代表该类内的所有样本;然后利用可以处理复杂分布数据的流形进化聚类(manifold evolutionary clustering,简称MEC)对第1 阶段得到的聚类中心进行类别划分;最后综合两次聚类结果整理得到最终聚类结果.该算法基于改进的K-均值算法和MEC 算法.在进化聚类算法的基础上引入流形距离,使得算法能够胜任复杂分布的数据聚类问题.同时,算法降低了引入流形距离所带来的计算量.在分布各异的7 个人工数据集和7 个UCI 数据集测试了二阶段聚类算法,并将其效果与遗传聚类算法、K 均值算法和流形进化聚类算法做了比较.实验结果表明,无论对于简单或复杂、凸或非凸的数据,TPC 都表现出良好的聚类性能,并且计算时间与MEC 相比明显减少.;In this paper, a Two-Phase Clustering (TPC) for the data sets with complex distribution is proposed. TPC contains two phases. First, the data set is partitioned into some sub-clusters with spherical distribution, and each clustering center represents all the members of its corresponding cluster. Then, by utilizing the outstanding clustering performance of the Manifold Evolutionary Clustering (MEC) for acomplex distributed data, the clustering centers obtained in the first phase are clustered. Finally, based on these two clustering results, the final results are obtained. This algorithm is based on an improved K-means, and the MEC. Manifold distance is introduced in evolutionary clustering to make the algorithm competent for the clustering of complex data sets. At the same time, the novel method reduces the computational cost brought by manifold distance. Experimental results on seven artificial data sets and seven UCI data sets with different structure show that the novel algorithm has the ability to identify clusters with simple or complex, convex, or non-convex distribution efficiently, compared with the genetic algorithm-based clustering, the K-means algorithm, and the manifold evolutionary clustering. Furthermore, TPC outperforms MEC obviously in terms of computational time.