Local Density Based Distributed Clustering Algorithm

Abstract: 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. Key words: distributed clustering; local density based clustering; local clustering model; density attractor; high dimension data 摘 要: 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