【摘要】 针对密度峰值聚类(DPC)算法存在的dc值难选择及近邻原则聚合操作在低密度区效果不佳的问题,提出一种基于人工蜂群与CDbw聚类指标优化的密度峰值聚类(BeeDPC)算法,以实现类簇间数据点的自动识别和合理聚类,并解决DPC对类簇间数据点类别识别上存在的缺陷.实验结果表明,BeeDPC算法具有自动识别并合理聚类类簇间数据点、自动识别类簇中心点和类簇数量及自动处理任意分布数据集的优势.更多还原

【Abstract】 Aiming at the problem that value of dc was difficult to select and the poor effect of neighborhood principle aggregation operation in low density area,we proposed a density peaks clustering(DPC)algorithm based on artificial bee colony and CDbw clustering index optimization,which realized automatic identification and reasonable clustering of data points between clusters,and solved the defect of DPC in class identification of data points between clusters.Experiment results show that the BeeDPC algorithm has advantages of automatic identification and reasonable clustering of data points between clusters,automatic identification of cluster centers and the number of clusters and dealing with arbitrary distributed data sets.更多还原