03439ntm a22005057i 4500 000725196 CZ-PrVSE 20250824103944.0 m d cr n|||||||||| 250824s2025 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Çırak, Elif ISIS:180221 dis Effect of outliers on hierarchical cluster analysis of qualitative data eng Effect of outliers on hierarchical cluster analysis of qualitative data / Elif Çırak 2025 ?? stran : digital, PDF soubor Vedoucí práce: Jana Cibulková Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2025 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2025 This thesis evaluates the effects of outliers on the performance of hierarchical clustering algorithms applied to qualitative data. Specifically, the study examines how the presence of outliers distorts clustering outcomes and whether the application of appropriate outlier detection techniques enhances cluster validity and structural stability. Hierarchical clustering, while widely used for exploratory analysis, is particularly sensitive to outliers, especially in the context of qualitative data where non-metric similarity measures and linkage methods are applied. This thesis investigates the influence of both individual and group outliers using synthetically generated categorical datasets and applies three detection techniques: Clustering-Based Outlier Detection, Local Outlier Factor, and Frequent Pattern Outlier Factor. Clustering performance is evaluated before and after outlier removal across various similarity (SMC, Lin, Eskin) and linkage combinations (single, complete, average). Internal (Silhouette Index, Dunn Index) and external (Adjusted Rand Index, Purity) validation metrics are employed to assess clustering quality. Results show that outliers negatively impact cluster quality, while targeted detection methods improve clustering robustness. The findings offer methodological guidance for analysts dealing with categorical data in domains such as market segmentation, social science, and health informatics. Způsob přístupu: Internet data analysis and modeling [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas Hierarchical Cluster Analysis Outliers in Clustering Qualitative Data Sensitivity to Outliers Cibulková, Jana ISIS:129091 ths Šulc, Zdeněk vse20191023055 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/89891/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/89891 Hlavní práce https://insis.vse.cz/zp/89891/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/89891/posudek/oponent/86451 Oponentura https://insis.vse.cz/zp/89891/podrobnosti dc:identifier NEPOSILAT VSKP vse89891 250821 89891