基于局部密度的高效聚类算法研究  

Research on Efficient Clustering Algorithm Based on Local Density

作　　者：周杰[1] 刘立波[1] 卜旭松 

机构地区：[1]宁夏大学数学计算机学院,宁夏银川750021

出　　处：《湖北工程学院学报》2015年第6期21-25,共5页Journal of Hubei Engineering University

摘　　要：针对基于密度聚类算法对参数值过于敏感、参数值难以设置的不足,提出一种基于局部密度的高效聚类算法,以有效解决基于密度聚类算法对密度层级不同数据集聚类准确率低的问题。该算法将局部密度概念和k-means算法相结合,在算法迭代过程中动态计算局部密度参数和簇核心距。对于分布在簇核心距之内的数据,采用基于划分的方法直接聚类;对于分布在簇核心距之外的数据采用基于局部密度方法进行聚类。实验结果表明,提出的算法在聚类精度和计算效率两方面均具有较好的性能。In view of the limitation of density-based clustering algorithm that is sensitive to parameter settings and difficult to set the parameters,this paper proposes an efficient local density-based clustering algorithm for the problem that the density-based clustering algorithm is not accurate to the data set with different density-levels.The proposed algorithm combines the local density concepts and K-means algorithms and dynamically calculates the local-density parameters and core-distance of the clusters in the process of the iteration.The data inside the core-distance is clustered into the clusters directly by the partition-based method and the data outside the core distance of the clusters is clustered by the local density method.Experimental results indicate that the proposed method exhibits better performance in both efficiency and accuracy.

关 键 词：K-MEANS 局部密度 簇核心距 聚类 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]