Analysis and improvement of evaluation indexes for clustering results

Zhong, Hao and Zhang, Huibing and Jia, Fei (2020) Analysis and improvement of evaluation indexes for clustering results. EAI Endorsed Transactions on Collaborative Computing, 4 (13): e4. ISSN 2312-8623

[img]
Preview
Text
eai.9-10-2017.163211.pdf - Published Version
Available under License Creative Commons Attribution No Derivatives.

Download (2MB) | Preview

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.

Item Type: Article
Uncontrolled Keywords: evaluation indexes, Calinski-Harabasz Index, Davies-Bouldin Index, Silhouette Coefficient
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 11 Sep 2020 09:16
Last Modified: 11 Sep 2020 09:16
URI: https://eprints.eudl.eu/id/eprint/242

Actions (login required)

View Item View Item