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master's thesis
Grupiranje srednjoškolaca i studenata prema njihovim interesima, fobijama i navikama klasterskom analizom
Zagreb: University of Zagreb, Faculty of Science, 2020. urn:nbn:hr:217:345432
Vlaić, Petra
University of Zagreb
Faculty of Science
Department of Mathematics

Institutional repository: Repository of the Faculty of Science

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Vlaić, P. (2020). Grupiranje srednjoškolaca i studenata prema njihovim interesima, fobijama i navikama klasterskom analizom (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:345432

Vlaić, Petra. "Grupiranje srednjoškolaca i studenata prema njihovim interesima, fobijama i navikama klasterskom analizom." Master's thesis, University of Zagreb, Faculty of Science, 2020. https://urn.nsk.hr/urn:nbn:hr:217:345432

Vlaić, Petra. "Grupiranje srednjoškolaca i studenata prema njihovim interesima, fobijama i navikama klasterskom analizom." Master's thesis, University of Zagreb, Faculty of Science, 2020. https://urn.nsk.hr/urn:nbn:hr:217:345432

Vlaić, P. (2020). 'Grupiranje srednjoškolaca i studenata prema njihovim interesima, fobijama i navikama klasterskom analizom', Master's thesis, University of Zagreb, Faculty of Science, accessed 23 December 2024, https://urn.nsk.hr/urn:nbn:hr:217:345432

Vlaić P. Grupiranje srednjoškolaca i studenata prema njihovim interesima, fobijama i navikama klasterskom analizom [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2020 [cited 2024 December 23] Available at: https://urn.nsk.hr/urn:nbn:hr:217:345432

P. Vlaić, "Grupiranje srednjoškolaca i studenata prema njihovim interesima, fobijama i navikama klasterskom analizom", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:217:345432

Metadata
TitleGrupiranje srednjoškolaca i studenata prema njihovim interesima, fobijama i navikama klasterskom analizom
AuthorPetra Vlaić
MentorAnamarija Jazbec (mentor)
Committee memberAnamarija Jazbec (predsjednik povjerenstva)
Committee memberBojan Basrak (član povjerenstva)
Committee memberSnježana Lubura Strunjak (član povjerenstva)
Committee memberMarko Horvat (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Mathematics)
Zagreb
Defense date and country2020-07-20, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
Klasterska analiza metoda je grupiranja objekata u klase tako da su u istoj klasi objekti najveće sličnosti. U ovom radu, klasterska analiza primijenjena je na podatcima o interesima, hobijima i zdravstvenim navikama studenata i srednjoškolaca. Podatci su prikupljeni koristeći online anketu koja je distribuirana putem društvenih mreža. Konačan broj analiziranih ispitanika je bio 699, pri čemu je omjer studenata i srednjoškolaca ravnomjeran. Za primjenu metode, potrebno je izabrati odgovarajuću mjeru sličnosti i algoritam ovisno o vrsti objekata, svojstvima algoritama te konačnim ciljevima istraživanja. Klasterski algoritmi mogu biti hijerarhijski ili nehijerarhijski (partitivni). Hijerarhijski algoritam je primijenjen koristeći Ward metodu, a rezultati su prikazani dendrogramom. Kao partitivni algoritam izabran je k-means s konačnim ograničenjima na dva, a potom i na tri klastera, dok je mjera sličnosti euklidska.
Abstract (english)
Cluster analysis is a method of grouping objects into classes so that the objects with the highest similarity are in the same class. In this paper, cluster analysis has been applied on a data set which consists of interests, hobbies and health habits of students and high school pupils. The data set was collected using an online survey which was distributed through social media. The final number of analyzed respondents was 699, having the ratio between students and high school pupils relatively equal. In order to apply the method, it is necessary to choose an adequate similarity measure and an algorithm depending on the type of data, features of the algorithm and main goals of the research. Clustering algorithms are either hierarchical or non-hierarchical (partitive). A hierarchical algorithm has been applied using the Ward method, and the results were shown on dendrograms. As for the type of partitive algorithm, the k-means algorithm was chosen, limiting the final number of clusters on two and on three, with the euclidean similarity measure.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:345432
Study programmeTitle: Mathematical Statistics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra matematike (magistar/magistra matematike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2020-11-30 13:53:39