Weiwei Ni, Geng Chen, Yingjie Wu, Sun Zhi-hui
Abstract
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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,对高维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
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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,对高维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
Key concepts: Cluster analysis, CURE data clustering algorithm, Computer science, Canopy clustering algorithm, Data mining, Data stream clustering, Correlation clustering, Fuzzy clustering