Comparison of selected similarity measures for hierarchical clustering of categorical data – Shahsuvar Abdullayev
Shahsuvar Abdullayev
Master's thesis
Comparison of selected similarity measures for hierarchical clustering of categorical data
Comparison of selected similarity measures for hierarchical clustering of categorical data
Abstract:
The aim of this thesis is to examine and compare the selected similarity measures for the hierarchical clustering of categorical data with variables that have more than two categories. Many the of categorical data clustering methods have not been researched well, because many of them are still in the phase of development. The analytical part of the thesis deals with the comparison of methods evaluated …moreAbstract:
The aim of this thesis is to examine and compare the selected similarity measures for the hierarchical clustering of categorical data with variables that have more than two categories. Many the of categorical data clustering methods have not been researched well, because many of them are still in the phase of development. The analytical part of the thesis deals with the comparison of methods evaluated …more
Language used: English
Date on which the thesis was submitted / produced: 5. 12. 2022
Identifier:
https://vskp.vse.cz/eid/88138
Thesis defence
- Date of defence: 30. 1. 2023
- Supervisor: Zdeněk Šulc
- Reader: Adam Čabla
Citation record
ISO 690-compliant citation record:
ABDULLAYEV, Shahsuvar. \textit{Comparison of selected similarity measures for hierarchical clustering of categorical data}. Online. Master's thesis. Praha: University of Economics, Prague. 2022. Available from: https://theses.cz/id/oy3522/.
Full text of thesis
Contents of on-line thesis archive
Published in Theses:- autentizovaným zaměstnancům ze stejné školy/fakulty
Other ways of accessing the text
Institution archiving the thesis and making it accessible: Vysoká škola ekonomická v Prazehttps://vskp.vse.cz/eid/88138
Vysoká škola ekonomická v Praze
Master programme / field:
Economic Data Analysis / Data Analysis and Modeling
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