基于图结构的分类数据距离度量

Distance Metric of Categorical Data Based on Graph Structure

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,New DM)。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New DM度量性能优于其他度量方法。

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.