一种改进的基于局部密度的聚类算法

关晓惠; 钱亚冠; 孙欣欣

doi:10.11959/j.issn.1000-0801.2016008

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一种改进的基于局部密度的聚类算法

研究与开发 | 更新时间：2024-06-05

- 一种改进的基于局部密度的聚类算法
- An improved clustering algorithm based on local density
- 电信科学 2016年32卷第1期页码：54-59
- 作者机构：
  
  1. 浙江水利水电学院，浙江杭州310018
  2. 浙江科技学院理学院，浙江杭州310023
- 作者简介：
  
  [ "关晓惠（1977-），女，浙江水利水电学院副教授，主要研究方向为机器学习与数据挖掘。" ]
  [ "钱亚冠（1976-），男，博士，浙江科技学院理学院副教授，主要研究方向为互联网流量分类、下一代互联网、机器学习与数据挖掘。" ]
  [ "孙欣欣（1973-），女，浙江水利水电学院副教授，主要研究方向为计算机网络。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2016008
  中图分类号： TN929.5
- 网络出版日期：2016-01，
  
  纸质出版日期：2016-01-20
- 稿件说明：
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关晓惠, 钱亚冠, 孙欣欣. 一种改进的基于局部密度的聚类算法[J]. 电信科学, 2016,32(1):54-59.

Xiaohui GUAN, Yaguan QIAN, Xinxin SUN. An improved clustering algorithm based on local density[J]. Telecommunications science, 2016, 32(1): 54-59.
关晓惠, 钱亚冠, 孙欣欣. 一种改进的基于局部密度的聚类算法[J]. 电信科学, 2016,32(1):54-59. DOI： 10.11959/j.issn.1000-0801.2016008.

Xiaohui GUAN, Yaguan QIAN, Xinxin SUN. An improved clustering algorithm based on local density[J]. Telecommunications science, 2016, 32(1): 54-59. DOI： 10.11959/j.issn.1000-0801.2016008.

摘要

聚类分析一直是机器学习和数据挖掘领域一个比较活跃而且极具挑战性的研究方向。Alex提出的基于局部密度的聚类算法是一种快速、有效的聚类方法，但该方法通过手工选取确定聚类个数和聚类中心。为此，对原算法进行改进，在初步选取候选聚类中心的基础上，使用基于密度连通的算法优化选取聚类中心，然后使用大密度最近邻方法确定样本类别。实验证明，该方法能有效解决聚类个数和聚类中心无法确定的问题，同时在聚类评价指标上显示出较好的聚类效果和性能。

Abstract

Clustering analysis is an important and challenging research field in machine learning and data mining.A fast and effective clustering algorithm based on the idea of local density was proposed by Alex.But the number of clusters and cluster centers in the algorithm were determined by hand.Therefore

the candidates of cluster centers based on local density were firstly selected and then density connectivity method was used to optimize the candidates.The classes of samples are the same as the nearest center with bigger local density.Experiments show that the proposed method has a better cluster efficiency and can handle the problems of uncertain cluster number and cluster centers.

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references

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