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master's thesis
Metode segmentacije podataka, te primjena na optimizaciju digitalnog marketinga u podatkovnom oblaku
Zagreb: University of Zagreb, Faculty of Science, 2021. urn:nbn:hr:217:232095
Braić, Drago
University of Zagreb
Faculty of Science
Department of Mathematics

Institutional repository: Repository of the Faculty of Science

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Braić, D. (2021). Metode segmentacije podataka, te primjena na optimizaciju digitalnog marketinga u podatkovnom oblaku (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:232095

Braić, Drago. "Metode segmentacije podataka, te primjena na optimizaciju digitalnog marketinga u podatkovnom oblaku." Master's thesis, University of Zagreb, Faculty of Science, 2021. https://urn.nsk.hr/urn:nbn:hr:217:232095

Braić, Drago. "Metode segmentacije podataka, te primjena na optimizaciju digitalnog marketinga u podatkovnom oblaku." Master's thesis, University of Zagreb, Faculty of Science, 2021. https://urn.nsk.hr/urn:nbn:hr:217:232095

Braić, D. (2021). 'Metode segmentacije podataka, te primjena na optimizaciju digitalnog marketinga u podatkovnom oblaku', Master's thesis, University of Zagreb, Faculty of Science, accessed 23 December 2024, https://urn.nsk.hr/urn:nbn:hr:217:232095

Braić D. Metode segmentacije podataka, te primjena na optimizaciju digitalnog marketinga u podatkovnom oblaku [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2021 [cited 2024 December 23] Available at: https://urn.nsk.hr/urn:nbn:hr:217:232095

D. Braić, "Metode segmentacije podataka, te primjena na optimizaciju digitalnog marketinga u podatkovnom oblaku", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:217:232095

Metadata
TitleMetode segmentacije podataka, te primjena na optimizaciju digitalnog marketinga u podatkovnom oblaku
Title (english)Data segmentation methods, and application to digital marketing optimization in the data cloud
AuthorDrago Braić
MentorSiniša Slijepčević (mentor)
Committee memberSiniša Slijepčević (predsjednik povjerenstva)
Committee memberNenad Antonić (član povjerenstva)
Committee memberGoran Muić (član povjerenstva)
Committee memberRudi Mrazović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Mathematics)
Zagreb
Defense date and country2021-03-04, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
U ovom radu obrađene su neke od metoda segmentacije podataka kao klase nenadziranog učenja, zajedno sa svojim matematičkim temeljima, te je prikazana njihova primjena na podacima o korisnicima kreditnih kartica. Rad započinje formalnim matematički zapisom problema, nakon čega se svaki od obrađenih algoritama detaljno opisuje. Posebna pažnja dana je k-means algoritmu i njegovim varijantama. Na kraju rada opisana je primjena navedenog algoritma koristeći programski jezik R. U navedenom primjeru prikazana je mogućnost grupiranja klijenata jedne banke na temelju demografskih karakteristika i zajedničkih obrazaca ponašanja, kao i mogućnost opisivanja velikog skupa podataka karakterističnim svojstvima nekoliko grupa dobivenih u procesu.
Abstract (english)
This paper discusses some of the data segmentation methods as a class of unsupervised learning, along with their mathematical foundations, and presents their application to credit card user data. The paper begins with a formal mathematical notation of the problem, after which each of the processed algorithms is described in detail. Special attention is given to the k-means algorithm and its variants. At the end of the paper, the application of the mentioned algorithm is described using the programming language R. The example shows the possibility of grouping bank’s clients based on demographic characteristics and common patterns of behavior, as well as the possibility of describing a large data set with the characteristic properties of several groups obtained in the process.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:232095
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 on2021-03-23 11:09:42