计算机工程与应用 ›› 2010, Vol. 46 ›› Issue (2): 113-117.DOI: 10.3778/j.issn.1002-8331.2010.02.035

• 数据库、信号与信息处理 • 上一篇    下一篇

I-Miner环境下聚类及可视化研究

侯天子,杨 燕,谭 维   

  1. 西南交通大学 信息科学与技术学院,成都 610031
  • 收稿日期:2008-07-24 修回日期:2008-10-17 出版日期:2010-01-11 发布日期:2010-01-11
  • 通讯作者: 侯天子

Research on clustering and visualization under I-Miner environment

HOU Tian-zi,YANG Yan,TAN Wei   

  1. School of Information Science and Technology,Southwest Jiaotong University,Chengdu 610031,China
  • Received:2008-07-24 Revised:2008-10-17 Online:2010-01-11 Published:2010-01-11
  • Contact: HOU Tian-zi

摘要: 聚类分析是数据挖掘中的核心技术,利用相关的可视化方法显示聚类结果,将数据分布以直观、形象的图形方式呈现给决策者,使得决策者可以直观地分析数据。I-Miner是一个企业级的数据挖掘工具,利用I-Miner软件进行聚类分析,并用多种方法将聚类结果可视化。通过S语言拓展软件功能,编程实现了K-Medoid算法、SOM算法、SOM与K-Medoids结合的聚类组合算法,尤其是在高维数据的可视化上,实现了星图法和SOM之U矩阵法,弥补软件中聚类和可视化模块较少的不足。

关键词: 数据挖掘, 聚类分析, 可视化, 自组织神经网络

Abstract: Clustering analysis is the core in data mining technology.By using the related visualization methods to visual clustering result,the distribution of data is presented in a directly and visually form in order to allow policy-maker to analysis data directly.I-Miner is an enterprise data mining tool,and it is used to clustering analysis or visual clustering result by kinds of methods.Through using the S language to develop software function,the K-Medoids,SOM,and SOM combining with K-Medoids algorithms are programmed.Particularly in the high dimensional data visualization,the star chart and the U-matrix by SOM are developed to deal with the defect of the software I-Miner about clustering and visual high dimensional data.

Key words: data mining, clustering analysis, visualization, self-organizing map

中图分类号: