An Improved K-Means Algorithm Based on Evidence Distance

An Improved K-Means Algorithm Based on Evidence Distance

The main influencing factors of the clustering effect of the k-means algorithm are the selection of the initial clustering center and the distance measurement between the sample points. The traditional k-mean algorithm uses Euclidean distance to measure the distance between sample points, thus it su...

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Bibliographic Details
Main Authors: Ailin Zhu, Zexi Hua, Yu Shi, Yongchuan Tang, Lingwei Miao
Format: Article
Language:English
Published: MDPI AG 2021-11-01
Series:Entropy
Subjects:
k-means clustering
evidence distance
cluster analysis
evidence theory
Online Access:https://www.mdpi.com/1099-4300/23/11/1550

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