cc 20(13): e4

Research Article

Analysis and improvement of evaluation indexes for clustering results

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  • @ARTICLE{10.4108/eai.9-10-2017.163211,
        author={Hao Zhong and Huibing Zhang and Fei Jia},
        title={Analysis and improvement of evaluation indexes for clustering results},
        journal={EAI Endorsed Transactions on Collaborative Computing},
        keywords={evaluation indexes, Calinski-Harabasz Index, Davies-Bouldin Index, Silhouette Coefficient},
  • Hao Zhong
    Huibing Zhang
    Fei Jia
    Year: 2020
    Analysis and improvement of evaluation indexes for clustering results
    DOI: 10.4108/eai.9-10-2017.163211
Hao Zhong1,*, Huibing Zhang2, Fei Jia2
  • 1: School of Computer Science, South China Normal University, Guangzhou 510631, China
  • 2: Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
*Contact email:


Clustering algorithm is the main field in collaborative computing of social network. How to evaluate clustering results accurately has become a hot spot in clustering algorithm research. Commonly used evaluation indexes are SC, DBI and CHI. There are two shortcomings in the calculation of three indexes. (1) Keep the number of clusters and the objects in the cluster unchanged. When transforming the feature vector, the three indexes will change greatly; (2) Keep the feature vector and the number of clusters unchanged. When changing the objects in the cluster, the three indexes will change tinily. This shows that the three indexes unable to evaluate the clustering results very well. Therefore, based on the calculation process of the three indexes, the paper proposes new three indexes - NSC, NDBI and NCHI. Through testing on standard data sets, three new indexes can better evaluate clustering results.