Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (4): 111-116.doi: 10.11896/jsjkx.200800011

• Database & Big Data & Data Science • Previous Articles     Next Articles

Fast Symbolic Data Clustering Algorithm Based on Symbolic Relation Graph

ZHANG Yan-jin1, BAI Liang1,2   

  1. 1 School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China
    2 Key Laboratory Computational Intelligence and Chinese Information Processing of Ministry of Education,Taiyuan 030006,China
  • Received:2020-06-24 Revised:2020-08-05 Online:2021-04-15 Published:2021-04-09
  • About author:ZHANG Yan-jin,born in 1995,postgraduate.Her main research interests include categorical data clustering.(zhang17836204220@163.com)
    BAI Liang，born in 1982，Ph.D，professor,is a member of China Computer Federation．His main research interests include cluster analysis and so on.
  • Supported by:
    National Natural Science Foundation of China (61773247,61876103) and Technology Research Development Projects of Shanxi (201901D211192).

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Abstract: Since a large amount of symbolic data is generated in practical applications,clustering of symbolicl data becomes an important research area of cluster analysis.Currently,many symbolic data clustering algorithms are proposed.When they are applied in big data environment,there are still problems such as high computational cost and slow operation speed.This paper proposes a fast symbolic data clustering algorithm based on symbolic relation graphs.It effectively solves this problem by replacing the original data with a symbolic relation graph and reducing the size of the data set.A large number of experiments show that the new algorithm is more effective than other algorithms.

Key words: Clustering, Data mining, Relation graph, Similarity measure, Symbolic data

CLC Number: 

  • TP391
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