Generalized fractional entropy functions with an application in hierarchical clustering

Publikationstyp
Journal Article
Date Issued
2023-06
Sprache
English
Author(s)
Chen, Churong  
Stojanow, Johannes Rolf 
Institut
Mathematik E-10  
TORE-URI
http://hdl.handle.net/11420/15074
Journal
Mathematical methods in the applied sciences  
Volume
46
Issue
9
Start Page
10074
End Page
10094
Citation
Mathematical Methods in the Applied Sciences 46 (9): 10074-10094 (2023-06)
Publisher DOI
10.1002/mma.9102
Scopus ID
2-s2.0-85148605695
We establish novel entropy functions based on some types of generalized fractional derivatives. Classic entropy functions are included in the results obtained in this paper. As an application in information theory and probability theory, we use these entropy functions to measure the uncertainty and similarity of data collected from crop-sown areas in 32 regions. For doing this, the approach of hierarchical clustering with the average-linkage method is used and three visual dendrograms are offered in the end.
Subjects
entropy function
generalized Hilfer-type fractional derivatives
generalized tempered Caputo-type fractional derivatives
generalized tempered Riemann–Liouville-type fractional derivatives