Journal of Guangdong University of Technology ›› 2023, Vol. 40 ›› Issue (04): 109-116.doi: 10.12052/gdutxb.220051

• Comprehensive Studies • Previous Articles     Next Articles

Distance Metric of Categorical Data Based on Graph Structure

Zheng Li-ping1, Deng Xiu-qin1, Zhang Yi-qun2   

  1. 1. School of Mathematics and Statistics, Guangdong University of Technology, Guangzhou 510520, China;
    2. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2022-03-17 Online:2023-07-25 Published:2023-08-02

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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.

Key words: categorical data, distance metric, graph structure, ordinal attribute

CLC Number: 

  • TP311.13
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