A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm

A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm

Abstract Clustering, a traditional machine learning method, plays a significant role in data analysis. Most clustering algorithms depend on a predetermined exact number of clusters, whereas, in practice, clusters are usually unpredictable. Although the Elbow method is one of the most commonly used m...

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Bibliographic Details
Main Authors: Congming Shi, Bingtao Wei, Shoulin Wei, Wen Wang, Hai Liu, Jialei Liu
Format: Article
Language:English
Published: SpringerOpen 2021-02-01
Series:EURASIP Journal on Wireless Communications and Networking
Subjects:
Machine learning
Clustering
Elbow method
Silhouette coefficient
Cosine law
Online Access:https://doi.org/10.1186/s13638-021-01910-w

Internet

https://doi.org/10.1186/s13638-021-01910-w

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