About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
cc 20(13): e4

Research Article

Analysis and improvement of evaluation indexes for clustering results

Download1460 downloads
Cite
BibTeX Plain Text
  • @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},
        volume={4},
        number={13},
        publisher={EAI},
        journal_a={CC},
        year={2020},
        month={2},
        keywords={evaluation indexes, Calinski-Harabasz Index, Davies-Bouldin Index, Silhouette Coefficient},
        doi={10.4108/eai.9-10-2017.163211}
    }
    
  • Hao Zhong
    Huibing Zhang
    Fei Jia
    Year: 2020
    Analysis and improvement of evaluation indexes for clustering results
    CC
    EAI
    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: scnuzhonghao@foxmail.com

Abstract

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.

Keywords
evaluation indexes, Calinski-Harabasz Index, Davies-Bouldin Index, Silhouette Coefficient
Received
2019-10-07
Accepted
2020-02-18
Published
2020-02-18
Publisher
EAI
http://dx.doi.org/10.4108/eai.9-10-2017.163211

Copyright © 2020 Hao Zhong et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL