A new validity clustering index-based on finding new centroid positions using the mean of clustered data to determine the optimum number of clusters

作者：

Highlights：

• A new CVI called VCIM is proposed to validate the clustering algorithm results.

• VCIM is designed to determine the optimal number of clusters.

• VCIM uses score function index and mean to find new cluster centroid positions.

• VCIM outperforms other well-known CVIs for both artificial and real-life datasets.

摘要

•A new CVI called VCIM is proposed to validate the clustering algorithm results.•VCIM is designed to determine the optimal number of clusters.•VCIM uses score function index and mean to find new cluster centroid positions.•VCIM outperforms other well-known CVIs for both artificial and real-life datasets.

论文关键词：Number of clusters,Clustering validity index,K-means,Fuzzy C-means,Hierarchical clustering

论文评审过程：Received 8 July 2020, Revised 21 November 2021, Accepted 26 November 2021, Available online 5 December 2021, Version of Record 10 December 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116329