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master's thesis
Klasterska analiza
Zagreb: University of Zagreb, Faculty of Science, 2016. urn:nbn:hr:217:703657
Ungaro, Tea
University of Zagreb
Faculty of Science
Department of Mathematics

Institutional repository: Repository of the Faculty of Science

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Ungaro, T. (2016). Klasterska analiza (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:703657

Ungaro, Tea. "Klasterska analiza." Master's thesis, University of Zagreb, Faculty of Science, 2016. https://urn.nsk.hr/urn:nbn:hr:217:703657

Ungaro, Tea. "Klasterska analiza." Master's thesis, University of Zagreb, Faculty of Science, 2016. https://urn.nsk.hr/urn:nbn:hr:217:703657

Ungaro, T. (2016). 'Klasterska analiza', Master's thesis, University of Zagreb, Faculty of Science, accessed 24 May 2024, https://urn.nsk.hr/urn:nbn:hr:217:703657

Ungaro T. Klasterska analiza [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2016 [cited 2024 May 24] Available at: https://urn.nsk.hr/urn:nbn:hr:217:703657

T. Ungaro, "Klasterska analiza", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2016. Available at: https://urn.nsk.hr/urn:nbn:hr:217:703657

Metadata
TitleKlasterska analiza
AuthorTea Ungaro
MentorAnamarija Jazbec (mentor)
Committee memberAnamarija Jazbec (predsjednik povjerenstva)
Committee memberDamir Bakić (član povjerenstva)
Committee memberPavle Goldstein (član povjerenstva)
Committee memberRobert Manger (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Mathematics)
Zagreb
Defense date and country2016-09-26, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
U ovom radu opisana je klasterska analiza te njena primjena na bazu dijabetičara podijeljenih prema stupnju bolesti. Klasterska analiza podrazumijeva grupiranje jedinki u klastere tako da su jedinke unutar klastera ”najsličnije”. Kod klasteriranja prvo moramo definirati udaljenost tj. sličnost između dvije jedinke, a nakon toga definiramo algoritam grupiranja. Postoji hijerarhijsko i nehijerarhijsko klasteriranje. U opisanom primjeru mjereno je 13 varijabli na osnovu kojih je bilo očekivano da će se pacijenti grupirati u četiri klastera (zdravi, dijabetes tipa I, dijabetes tipa II, predijabetes). Napravljeni su hijerarhijski klasterinzi i to pomoću euklidske mjere udaljenosti i metode prosjeka, Gower mjere udaljenosti i Wardove metode klasteriranja, te Manhattan mjere udaljenosti i metode maksimuma, kao i nehijerarhijski klasterinzi algoritmom k-srednjih vrijednosti za k=4 i 3.
Abstract (english)
This paper describes cluster analysis and its application to the base of diabetics divided according to degree of the disease. Cluster analysis involves grouping elements into clusters so the elements within one cluster are the most similar. For clustering we must first define the distance (similarity) between two elements and then define the grouping algorithm. There are hierarchical and nonhierarchical clustering. In described example there were 13 variables measured from which was expected that patients will be grouped into four clusters (healthy, type I diabetes, type II diabetes, prediabetes). Hierarchical clustering was made with the Euclidian distance and the average method, the Gower distance and the Ward method and the Manhattan distance and the maximum method. Also, nonhierarchical clustering was made with k-means algorithm for k=4 and 3.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:703657
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 on2019-02-18 11:35:57