Loading...
Loading...

Go to the content (press return)

Automatic identification of the number of clusters in hierarchical clustering

Author
Karna, A.; Gibert, Karina
Type of activity
Journal article
Journal
Neural computing and applications
Date of publication
2021-03-13
First page
1
Last page
16
DOI
10.1007/s00521-021-05873-3
URL
https://link.springer.com/article/10.1007%2Fs00521-021-05873-3 Open in new window
Abstract
Hierarchical clustering is one of the most suitable tools to discover the underlying true structure of a dataset in the case of unsupervised learning where the ground truth is unknown and classical machine learning classifiers are not suitable. In many real applications, it provides a perspective on inner data structure and is preferred to partitional methods. However, determining the resulting number of clusters in hierarchical clustering requires human expertise to deduce this from the dendrog...
Keywords
3D printing, Calinski-Harabasz index, Data science, Decision support, Hierarchical clustering, Scalability
Group of research
KEMLG - Knowledge Engineering and Machine Learning Group
Universitat Politècnica de Catalunya

Participants