Usporedba algoritama nenadziranog strojnog učenja pri segmentaciji kupaca

Marković, Marko

prikaz prve stranice dokumenta Usporedba algoritama nenadziranog strojnog učenja pri segmentaciji kupaca

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master's thesis

Usporedba algoritama nenadziranog strojnog učenja pri segmentaciji kupaca

diplomski rad

Split: University of Split, Faculty of economics Split, 2022. urn:nbn:hr:124:906686

Marković, Marko

University of Split
Faculty of economics Split

Cite this document

APA 6th Edition

Marković, M. (2022). Usporedba algoritama nenadziranog strojnog učenja pri segmentaciji kupaca : diplomski rad (Master's thesis). Split: University of Split, Faculty of economics Split. Retrieved from https://urn.nsk.hr/urn:nbn:hr:124:906686

MLA 8th Edition

Marković, Marko. "Usporedba algoritama nenadziranog strojnog učenja pri segmentaciji kupaca : diplomski rad." Master's thesis, University of Split, Faculty of economics Split, 2022. https://urn.nsk.hr/urn:nbn:hr:124:906686

Chicago 17th Edition

Harvard

Marković, M. (2022). 'Usporedba algoritama nenadziranog strojnog učenja pri segmentaciji kupaca : diplomski rad', Master's thesis, University of Split, Faculty of economics Split, accessed 23 March 2025, https://urn.nsk.hr/urn:nbn:hr:124:906686

Vancouver

Marković M. Usporedba algoritama nenadziranog strojnog učenja pri segmentaciji kupaca : diplomski rad [Master's thesis]. Split: University of Split, Faculty of economics Split; 2022 [cited 2025 March 23] Available at: https://urn.nsk.hr/urn:nbn:hr:124:906686

IEEE

M. Marković, "Usporedba algoritama nenadziranog strojnog učenja pri segmentaciji kupaca : diplomski rad", Master's thesis, University of Split, Faculty of economics Split, Split, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:124:906686

Cite this item: https://urn.nsk.hr/urn:nbn:hr:124:906686

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Title	Usporedba algoritama nenadziranog strojnog učenja pri segmentaciji kupaca : diplomski rad
Author	Marko Marković
Mentor	Željko Garača (mentor) MBZ: 148451
Granter	University of Split Faculty of economics Split Split
Defense date and country	2022-09, Croatia
Scientific / art field, discipline and subdiscipline	SOCIAL SCIENCES Economics Business Informatics
Abstract	Segmentacija tržišta jedan je od glavnih procesa marketinga. Krajem 20. i početkom 21. stoljeća razvija se područje umjetne inteligencije i strojnog učenja. Nenadzirano strojno učenje se pokazalo kao jedna od grana koja je iznimno dobra u prepoznavanju i grupiranju uzoraka. K-Means i Hijerarhijski algoritam jedni su od najčešće korištenih algoritama u primjeni pri segmentaciji potrošača. Ovakvi algoritmi se često smatraju rezerviranim za velike baze podataka. Cilj ovog rada bio je ispitati karakteristike primjene ovakvih algoritama pri segmentaciji potrošača na manjim setovima podataka. Sukladno tomu usporediti algoritme s obzirom na rezultate segmentacije i dati odgovore na istraživačka pitanja. Pri ovom istraživanju korištena su dva seta podataka, jedan sa 500, a drugi sa 200 unosa. Svaki set sadržao je 5 varijabli. Setovi su obrađeni putem K-Means i Hijerarhijskog algoritma unutar programskog jezika Python. Primjena algoritama bila je moguća na manjim setovima podataka, ali uz kompromise u preciznosti, efikasnosti i jednostavnosti upotrebe.
Abstract (english)	Market segmentation is one of the main marketing processes. At the end of the 20th and beginning of the 21st century, the field of artificial intelligence and machine learning has greatly developed. Unsupervised machine learning has proven to be one branch that is extremely good at recognizing and grouping patterns. K-Means and the Hierarchical Algorithm are one of the most commonly used algorithms in consumer segmentation. Such algorithms are often considered to be reserved for large databases. The aim of this paper was to examine the characteristics of the application of such algorithms in consumer segmentation on smaller data sets. Accordingly, compare algorithms with respect to segmentation results and provide answers to research questions. In this research, two sets of data were used, one with 500 and the other with 200 entries. Each set contained 5 variables. The sets were processed using K-Means and the Hierarchical Algorithm within the Python programming language. Application of algorithms was possible on smaller data sets, but with compromises in precision, efficiency and ease of use.
Keywords
Keywords (english)
Language	croatian
URN:NBN	urn:nbn:hr:124:906686
Study programme	Title: Business Studies Study programme type: university Study level: graduate Academic / professional title: magistar/magistra ekonomije (magistar/magistra ekonomije)
Type of resource	Text
File origin	Born digital
Access conditions	Access restricted to students and staff of home institution
Terms of use
Repository	REFST
Created on	2022-11-22 10:18:05