03512ntm a22005537i 4500 000658922 CZ-PrVSE 20200209092947.0 m d cr n|||||||||| 200209s2019 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Žárský, Jiří ISIS:107173 dis Evaluace srozumitelnosti pro shlukování textu eng Evaluation of text clustering comprehensibility / Jiří Žárský 2019 ?? stran : digital, PDF soubor Vedoucí práce: Tomáš Kliegr Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2020 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2020 This master thesis aims to evaluate three methods for visualizing results of text clustering, which is an unsupervised task of mining textual data, by conducting a user study. Before conceiving visualizations, clustering analysis of the “Russian Troll tweets” data set was performed. The analysis was completed using Python in accordance with the CRISP-DM methodology. The visualizations investigated in the user study were word clouds computed from TF-IDF frequencies or their Z-scores and Z-scores shown as bar charts. The author himself designed word clouds calculated from Z-scores; other visualizations were created based on available literature. Survey methodology was adapted from the study of comprehensibility of decision trees by Piltaver et al. (2016)* and included “classify”, “explain” and “validate” tasks, and three measures (correct answers, subjective comprehensibility, and time required). Hypotheses, the procedure, and planned analyses were pre-registered before data collection at osf.io, and participants were recruited via crowdsourcing. Based on responses from 188 participants, word clouds generated from Z-scores were found to produce the most comprehensible visualizations of textual data clusters.* PILTAVER, Rok, LUŠTREK, Mitja, GAMS, Matjaž and MARTINČIĆ-IPŠIĆ, Sanda. What makes classification trees comprehensible? Expert Systems with Applications. November 2016. Vol. 62, p. 333–346. DOI 10.1016/j.eswa.2016.06.009. Způsob přístupu: Internet informační systémy a technologie [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas text mining comprehensibility Clustering crowd sourcing data mining machine learning Z-score word cloud Kliegr, Tomáš ISIS:8484 ths Bahník, Štěpán xx0194885 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/66286/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/66286 Hlavní práce https://insis.vse.cz/zp/66286/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/66286/posudek/oponent/64748 Oponentura https://insis.vse.cz/zp/66286/podrobnosti dc:identifier NEPOSILAT VSKP vse66286 200204 66286