一种基于EFD的混合属性聚类算法
Clustering algorithm for mixed attribute based on EFD
王文庆 1向孜瑞1
作者信息
- 1. 西安邮电大学 自动化学院,陕西 西安 710121;物联网应用技术联合示范实验室,陕西 西安 710121
- 折叠
摘要
为了提高混合属性聚类效率,提出一种基于扩张翻转距离(Expand Flip Distance,EFD)的混合属性聚类算法.以信息熵及熵权法为基础,通过定义扩张属性和属性扩张量得到 EFD,将其作为待聚类对象属性区分的依据,进行聚类对象的属性约简,最终对约简后的属性构建混合属性聚类模型,实现混合属性聚类.实验结果表明,所提算法获得的聚类谱系图和聚类结果均优于对比算法,验证了该算法的合理性和有效性.
Abstract
In order to improve the clustering efficiency of mixed attributes,a mixed-attribute cluste-ring algorithm based on the expand flip distance(EFD)is proposed.Based on the information entro-py and entropy weight method,the EFD is obtained by defining the expansion attribute and the at-tribute expansion amount,which is used as the basis for the attribute differentiation of the object to be clustered,and the attribute reduction of the clustered object is carried out,and finally the mixed attribute clustering model is constructed for the reduced attribute to realize the mixed attribute clus-tering.Experiment results show that the clustering pedigree map and the clustering results obtained by the proposed algorithm are better than those of the comparison algorithms,which verifies its ra-tionality and effectiveness.
关键词
混合属性聚类/扩张属性/属性扩张量/扩张翻转距离/属性差异化Key words
mixed attribute clustering/expansion properties/the amount of attribute expansion/ex-pand flip distant/attribute differentiation引用本文复制引用
基金项目
陕西省重点研发计划(2018ZDXM-GY-039)
出版年
2024
NETL
NSTL
维普
万方数据