基于证据空间有效性指标的聚类选择性集成

毕凯; 王晓丹; 邢雅琼

doi:10.11959/j.issn.1000-436x.2015146

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基于证据空间有效性指标的聚类选择性集成

学术论文 | 更新时间：2024-06-05

- 基于证据空间有效性指标的聚类选择性集成
- Cluster ensemble selection based on validity index in evidence space
- 通信学报 2015年36卷第8期页码：135-145
- 作者机构：
  
  空军工程大学防空反导学院，陕西西安710051
- 作者简介：
  
  [ "毕凯（1985-），男，河南南阳人，空军工程大学博士生，主要研究方向为模式识别与智能信息处理。" ]
  [ "王晓丹（1966-），女，陕西汉中人，空军工程大学教授、博士生导师，主要研究方向为智能信息处理和机器学习等。" ]
  [ "邢雅琼（1986-），女，陕西渭南人，空军工程大学博士生，主要研究方向为智能信息处理和机器学习等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(60975026);国家自然科学基金资助项目(61273275)
- DOI：10.11959/j.issn.1000-436x.2015146
  中图分类号： TP391
- 网络出版日期：2015-08，
  
  纸质出版日期：2015-08-25
- 稿件说明：
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毕凯, 王晓丹, 邢雅琼. 基于证据空间有效性指标的聚类选择性集成[J]. 通信学报, 2015,36(8):135-145.

Kai BI, Xiao-dan WANG, Ya-qiong XING. Cluster ensemble selection based on validity index in evidence space[J]. Journal on communications, 2015, 36(8): 135-145.
毕凯, 王晓丹, 邢雅琼. 基于证据空间有效性指标的聚类选择性集成[J]. 通信学报, 2015,36(8):135-145. DOI： 10.11959/j.issn.1000-436x.2015146.

Kai BI, Xiao-dan WANG, Ya-qiong XING. Cluster ensemble selection based on validity index in evidence space[J]. Journal on communications, 2015, 36(8): 135-145. DOI： 10.11959/j.issn.1000-436x.2015146.

摘要

首先针对距离空间在描述数据复杂结构信息方面的不足给出证据空间的概念。然后基于证据空间扩展有效性指标 Davies-Bouldin，同时利用聚类成员的类别相关矩阵度量差异性。最后以较高有效性和较大差异性为目标选择聚类成员并用于集成。实验结果显示所提方法能够有效提高聚类集成算法的有效性。

Abstract

At first

the concept of evidence space was proposed to overcome the weakness of distance space for describing the complex structure of data sets.And then

the Davies-Bouldin index was extended based on the evidence space proposed.Meanwhile the label-correlation matrix was used to measure the difference of clusters members.At last

the cluster members with better effectiveness and bigger differences were selected for cluster ensemble.The experimental results show that the proposed method is able to improve the effectiveness of cluster ensemble.

关键词

Keywords

references

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