基于改进流形距离和人工蜂群的二阶段聚类算法

Two-phase clustering algorithm based on the improved manifold distance and the artificial bee colony algorithm

以改进的流形距离为相似度测度,结合人工蜂群算法,提出一种二阶段聚类算法.首先根据局部密度、最大最小距离和近邻选择对数据集初步归类并得到簇代表点;然后将聚类归属为优化问题,通过改进的蜂群算法对簇代表点及没归类的样本点较快地搜索到最优聚类中心,同时根据流形距离的全局一致性特征,对样本进行精确的类别划分;最后将两阶段算法综合归类.实验结果表明,所提出的算法可以获得良好的聚类效果.

A two-phase clustering algorithm based on the improved manifold distance as the similarity measure combined with the bee colony algorithm is proposed. Firstly, based on local density, max-rain distance and neighbors selecting, data set is initialized, and the representative points are obtained. Then, the clustering algorithm is viewed as an optimization problem, in which the correctly category is obtained by getting the optimal clustering center through the improved bee colony algorithm dealing with the representative and unclassified points, and obtaining the overall consistency information of the manifold distance. Finally, the two phase algorithms are classified. Experiment results show that the proposed algorithm has better clustering results.