04253ntm a22005777i 4500 000345338 CZ-PrVSE 20171029225108.0 m d cr n|||||||||| 171029s2016 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Šulc, Zdeněk vse20191023055 dis Míry podobnosti pro nominální data v hierarchickém shlukování eng Similarity Measures for Nominal Data in Hierarchical Clustering / Zdeněk Šulc 2016 ?? stran : digital, PDF soubor Vedoucí práce: Hana Řezanková Disertační práce (Ph.D.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2017 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2017 This dissertation thesis deals with similarity measures for nominal data in hierarchical clustering, which can cope with variables with more than two categories, and which aspire to replace the simple matching approach standardly used in this area. These similarity measures take into account additional characteristics of a dataset, such as frequency distribution of categories or number of categories of a given variable. The thesis recognizes three main aims. The first one is an examination and clustering performance evaluation of selected similarity measures for nominal data in hierarchical clustering of objects and variables. To achieve this goal, four experiments dealing both with the object and variable clustering were performed. They examine the clustering quality of the examined similarity measures for nominal data in comparison with the commonly used similarity measures using a binary transformation, and moreover, with several alternative methods for nominal data clustering. The comparison and evaluation are performed on real and generated datasets. Outputs of these experiments lead to knowledge, which similarity measures can generally be used, which ones perform well in a particular situation, and which ones are not recommended to use for an object or variable clustering. The second aim is to propose a theory-based similarity measure, evaluate its properties, and compare it with the other examined similarity measures. Based on this aim, two novel similarity measures, Variable Entropy and Variable Mutability are proposed; especially, the former one performs very well in datasets with a lower number of variables. The third aim of this thesis is to provide a convenient software implementation based on the examined similarity measures for nominal data, which covers the whole clustering process from a computation of a proximity matrix to evaluation of resulting clusters. This goal was also achieved by creating the nomclust package for the software R, which covers this issue, and which is freely available. Způsob přístupu: Internet statistika [obor disert. práce] disertace fd132024 czenas dissertations eczenas object clustering variable clustering an R package nominal data similarity measures hierarchical cluster analysis evaluation criteria data generation Řezanková, Hana, 1956- ola2004231522 ths Šimůnek, Milan, 1975- ola2003200941 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/42029/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/42029 Hlavní práce https://insis.vse.cz/zp/42029/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/42029/posudek/oponent/49819 Oponentura https://insis.vse.cz/zp/42029/posudek/oponent/49820 Oponentura https://insis.vse.cz/zp/42029/podrobnosti dc:identifier NEPOSILAT VSKP vse42029 171027 42029