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山东大学学报(工学版) ›› 2011, Vol. 41 ›› Issue (4): 85-90.

• 论文 • 上一篇    下一篇

基于视觉原理的密度聚类算法的改进

蒋盛益1,罗方伦1,余雯2   

  1. 广东外语外贸大学 1. 信息学院; 2. 国际工商管理学院, 广东 广州 510006
  • 收稿日期:2011-02-01 出版日期:2011-08-16 发布日期:2011-02-01
  • 作者简介:蒋盛益(1963- ),男,湖南隆回人,教授,博士,主要研究方向为数据挖掘与自然语言处理. E-mail:jiangshengyi@163.com
  • 基金资助:

    国家自然科学基金资助项目(61070061);广东省自然科学基金资助项目(9151026005000002);广东省高层次人才资助项目

Enhanced visual-based density clustering algorithm

JIANG Sheng-yi1, LUO Fang-lun1, YU  Wen2   

  1. 1. School of Informatics; 2. School of Management, Guangdong University of Foreign Studies, Guangzhou 510006, China
  • Received:2011-02-01 Online:2011-08-16 Published:2011-02-01

摘要:

结合基于视觉原理的密度聚类算法对初始化参数不敏感、能发现任意形状的聚类、能够找出最优聚类及一趟聚类算法快速高效的特点,研究可以处理混合属性的高效聚类算法.首先简单改进基于视觉原理的密度聚类算法,使之可以处理含分类属性的数据,进而提出一种两阶段聚类算法。第一阶段使用一趟聚类算法对数据集进行初始划分,第二阶段利用基于视觉原理的密度聚类算法归并初始划分而得到最终聚类。在真实数据集和人造数据集上的实验结果表明,提出的两阶段聚类算法是有效可行的。

关键词: 一趟聚类算法, 视觉原理聚类, 任意形状簇

Abstract:

 Visual-based density clustering algorithm is insensitive to the initialized parameters, identify the data with any shape and can find the optimal cluster. One-pass clustering algorithm is efficient and fast. Based on their features we do research on a clustering algorithm which can process the data with mixing attributes. At first, the visual-based density clustering algorithm was improved slightly, which enabled it to process the data with categorical attributes. Then, the two-stage clustering algorithm was put forward. In the first stage, single pass clustering algorithm was used to group the data as an original partition. In the second stage, improved visual-based density clustering algorithm was used to merge the original partition so that the clusters are finally obtained. The experimental results of both the actual and synthetic datasets show that the presented clustering algorithm is effective and practicable.

Key words:  single pass clustering algorithm, visual theory clustering, arbitrary shape cluster

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