A Density-based Improvement for Distributed Clustering Algorithm

LIU Heng; SU Jing; TANG Xian-yan; LU Jia-jia; LIANG Zhi-sheng; HONG Yue-hua

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A Density-based Improvement for Distributed Clustering Algorithm

更新时间：2026-09-04

- A Density-based Improvement for Distributed Clustering Algorithm
- Vol. 35, Issue 5, Pages: 128-132(2018)
- 作者机构：
  
  1. 广西医科大学机构信息与管理学院,广西,南宁,530021
  2. 广西医科大学公共卫生学院,广西,南宁,530021
  3. 广西医科大学研究生院,广西,南宁,530021
  4. 广西医科大学信息中心,广西,南宁,530021
  5. 广西经济管理干部学院机构计算机系,广西,南宁,530007
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2018
- 稿件说明：
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刘恒, 苏静, 唐咸艳, 卢佳佳, 梁志胜, 洪月华. 一种基于密度的分布式聚类改进算法[J]. 微电子学与计算机, 2018, 35(5): 128-132.

LIU Heng, SU Jing, TANG Xian-yan, et al. A Density-based Improvement for Distributed Clustering Algorithm[J]. 2018, 35(5): 128-132.
刘恒, 苏静, 唐咸艳, 卢佳佳, 梁志胜, 洪月华. 一种基于密度的分布式聚类改进算法[J]. 微电子学与计算机, 2018, 35(5): 128-132. DOI：

LIU Heng, SU Jing, TANG Xian-yan, et al. A Density-based Improvement for Distributed Clustering Algorithm[J]. 2018, 35(5): 128-132. DOI：

摘要

基于密度分布式聚类算法(DBDC) 在分布式聚类运算中运用广泛

但具有较高的算法时间复杂度.本文提出了一种基于代表点交互的全局聚类方法

利用数据网格运算方法将数据对象映射到空间网格

改进基于密度的分布式聚类算法

优化原算法的空间搜索过程

从而改进生成本地聚类的效率.同时在全局聚类层面可利用中心点作为代表点来降低聚类误差.通过实验

表明该方法能够改进基于密度的分布式聚类算法

较传统分布式聚类算法提高了准确性度并降低了算法时间复杂度.

Abstract

The density based distributed clustering algorithm (DBDC) has been employed widely in process of distributed clustering.A major drawback of DBDC is the high time complexity.This work presented a method based on representative point for global clustering.A data grid mapping method which mapped data object to space grid

which improved DBDC and enhance the space search process and efficiency of implementation in local clustering level.Clustering error can be reduced by aglobal clustering method based on representative point.Using verification experiment with real data set from open source software

results showed this algorithm can improve original DBDC in two aspects: accuracy and reducing time complexity.

关键词

Keywords

references

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Yongli Li, Peng Sun, Minghui Sun, Rui Mao, et. al, An improved distributed clustering algorithm based on density[C], the proceeding of 8th International Conference on Intelligent Networks and Intelligent Systems, 2015, pp133-136.

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邓左祥, 李春贵. 一种有效的多关系聚类算法[J]. 微电子学与计算机, 2016, 33 (4): 33-41. DOI: 33

吉贵, 刘杰, 赵连字. 聚类算法研究[J]. 软件学报, 2008, 19 (1): 48-61. DOI: 48

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