基于图结构的分类数据距离度量

郑丽苹; 邓秀勤; 张逸群

doi:10.12052/gdutxb.220051

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基于图结构的分类数据距离度量

综合研究 | 更新时间：2026-05-27

- 基于图结构的分类数据距离度量
- Distance Metric of Categorical Data Based on Graph Structure
- 广东工业大学学报 2023年40卷第4期页码：109-116
- 作者机构：
  
  1. 广东工业大学数学与统计学院,广东,广州,510520
  2. 广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  郑丽苹(1998–)，女，硕士研究生，主要研究方向为机器学习、数据挖掘
  邓秀勤(1966–)，女，教授，本科，主要研究方向为机器学习、数据挖掘，E-mail：dxq706@gdut.edu.cn
- 基金信息：
  
  广东省研究生教育创新计划项目 (2021SFKC030);广东省自然科学基金资助面上项目(2022A1515011592)
- DOI：10.12052/gdutxb.220051
  中图分类号： TP311.13
- 网络首发：2023-07-25，
  
  纸质出版：2023
- 稿件说明：
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郑丽苹, 邓秀勤, 张逸群. 基于图结构的分类数据距离度量[J]. 广东工业大学学报, 2023, 40(4): 109-116.

Zheng Li-ping, Deng Xiu-qin, Zhang Yi-qun. Distance Metric of Categorical Data Based on Graph Structure[J]. 2023, 40(4): 109-116.
郑丽苹, 邓秀勤, 张逸群. 基于图结构的分类数据距离度量[J]. 广东工业大学学报, 2023, 40(4): 109-116. DOI： 10.12052/gdutxb.220051.

Zheng Li-ping, Deng Xiu-qin, Zhang Yi-qun. Distance Metric of Categorical Data Based on Graph Structure[J]. 2023, 40(4): 109-116. DOI： 10.12052/gdutxb.220051.

摘要

针对现有的大多数分类数据的度量方法效果不佳的问题，本文提出了一种基于有序属性和标称属性图结构的分类数据距离度量方法(New Distance Metric，NewDM) 。首先总结了分类数据距离定义的基本框架公式并分析度量该类型数据的挑战，然后利用不同属性的图结构定义了2个概率分布列距离，紧接着联立权重给出了分类数据的距离度量新方法，最后在6个公开数据集上进行实验，结果表明本文提出的NewDM度量性能优于其他度量方法。

Abstract

This paper proposes a new distance metric (NewDM) of categorical data based on the graph structure of ordinal and nominal attributes to address the poor effect of most existing measurement methods for categorical data. In NewDM

it first summarizes the basic framework formula of distance definition of categorical data and analyzes the challenges of measuring categorical data. Then

the graph structures of ordinal attributes and nominal ones are utilized to define the distance between two probability distribution columns. Finally

a new distance metric of categorical data is obtained through simultaneous weight. Experimental results on six public datasets show that the proposed NewDM is superior to the state-of-the-art approaches.

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references

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