一种基于局部密度的分布式聚类挖掘算法

分布式聚类挖掘技术是解决数据集分布环境下聚类挖掘问题的有效方法.针对数据水平分布情况,在已有分布式密度聚类算法DBDC(density based distributed clustering)的基础上,引入局部密度聚类和密度吸引子等概念,提出一种基于局部密度的分布式聚类算法——LDBDC(local density based distributed clustering).算法适用于含噪声数据和数据分布异常情况,对高雏数据有着良好的适应性.理论分析和实验结果表明,LDBDC算法在聚类质量和算法效率方面优于已有的DBDC算法和SDBDC(scalable dellsity-based distributed clustering)算法.算法是有效、可行的.

Local Density Based Distributed Clustering Algorithm

Distributed clustering is an effect method for solving the problem of clustering data located at different sites.Considering the circumstance that data is horizontally distributed,algorithm LDBDC(local density based distributed clustering)is presented based on the existeding algorithm DBDC(density based distributed clustering), which can easily fit datasets of high dimension and abnormal distribution by adopting ideas such as local density-based clustering and density attractor.Theoretical analysis and experimental results show that algorithm LDBDC outperforms DBDC and SDBDC(scalable density-based distributed clustering)in both clustering quality and efficiency.