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master's thesis
Analiza i rudarenje podataka o projektima financiranim kroz Erasmus+ program
2017. urn:nbn:hr:124:534454
Župančić, Erik
University of Split
Faculty of economics Split

Cite this document

Župančić, E. (2017). Analiza i rudarenje podataka o projektima financiranim kroz Erasmus+ program (Master's thesis). Split: University of Split, Faculty of economics Split. Retrieved from https://urn.nsk.hr/urn:nbn:hr:124:534454

Župančić, Erik. "Analiza i rudarenje podataka o projektima financiranim kroz Erasmus+ program." Master's thesis, University of Split, Faculty of economics Split, 2017. https://urn.nsk.hr/urn:nbn:hr:124:534454

Župančić, Erik. "Analiza i rudarenje podataka o projektima financiranim kroz Erasmus+ program." Master's thesis, University of Split, Faculty of economics Split, 2017. https://urn.nsk.hr/urn:nbn:hr:124:534454

Župančić, E. (2017). 'Analiza i rudarenje podataka o projektima financiranim kroz Erasmus+ program', Master's thesis, University of Split, Faculty of economics Split, accessed 07 April 2025, https://urn.nsk.hr/urn:nbn:hr:124:534454

Župančić E. Analiza i rudarenje podataka o projektima financiranim kroz Erasmus+ program [Master's thesis]. Split: University of Split, Faculty of economics Split; 2017 [cited 2025 April 07] Available at: https://urn.nsk.hr/urn:nbn:hr:124:534454

E. Župančić, "Analiza i rudarenje podataka o projektima financiranim kroz Erasmus+ program", Master's thesis, University of Split, Faculty of economics Split, Split, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:124:534454

Metadata
TitleAnaliza i rudarenje podataka o projektima financiranim kroz Erasmus+ program
AuthorErik Župančić
MentorMaja Ćukušić (mentor)
MBZ: 300571
GranterUniversity of Split
Faculty of economics Split
Split
Defense date and country2017-08, Croatia
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Economics
Business Informatics
Abstract
Suvremena poduzeća traže način na koji će korištenjem ogromnih količina dostupnih podataka steći konkurentske prednosti. Jedan od načina za ostvarenje tog cilja je korištenje tehnika rudarenja podataka. Aktivnosti rudarenja podataka (eng. Data Mining) predstavljaju iterativni proces usmjeren prema analizi velikih količina podataka s ciljem izvlačenja korisnih informacija i znanja, koji se mogu pokazati korisnim za rješavanje problema i donošenje odluka. Pri tom se koristi širokom lepezom... More alata kao što su asocijacije, grupiranja i stabla odlučivanja, kako bi postiglo svoje ciljeve. Prema CRISP-DM metodologiji, uobičajeni proces rudarenja podataka sastoji se od šest iterativnih koraka. Prvi korak je razumijevanje poslovanja gdje je cilj shvatiti što se želi rudarenjem podataka postići iz poslovne perspektive. Druga faza, razumijevanje podataka, odnosi se na prikupljanje i opće sagledavanje podataka. Treća faza, priprema podataka, svodi se na pretvaranje podataka u oblik pogodan za analizu i rudarenje. Četvrta faza, modeliranje, sastoji se od odabira tehnika rudarenja, stvaranje modela rudarenja podataka i pokretanja tog modela. Peta faza, procjena, utvrđuje se stupanj zadovoljavanja poslovnih ciljeva, Posljednja faza, implementacija, identificira način na koji će se stvoreni rezultati koristiti. Ti su teorijski koncepti praktično primijenjeni na primjeru skupa podataka o općim karakteristikama Erasmus+ projekata. Provođenjem eksplorativne analize na primjeru dolazi se do rezultata o učestalim karakteristikama projekata, dok se dubljim rudarenjem podataka otkrivaju skriveni obrasci u podacima. Less
Abstract (english)
Modern corporations seek for a way to use huge amounts of available data to accrue competitive advantages. One way of doing it is by using data mining. The term data mining refers to the overall process consisting of data gathering and analysis, development of inductive learning models and adoption of practical decisions and consequent actions bade on the knowledge acquired. It uses wide array of tools such as associations, clustering and decision trees, to achieve its goals. Usual... More data mining process according to the CRISP-DM methodology consists of six iterative phases. First phase is business understanding where the goal is to understand what's expected to accomplish from a business perspective. Second phase, data understanding, requires acquiring the data listed in the project resources and analyse it. Third phase, data preparation, is where the decided data is being prepared for analysis. Fourth phase, modelling, consists of choosing modelling techniques, creating a data mining model, and running that model. Fifth phase, evaluation, assesses the degree to which the model meets business objectives. Last phase, deployment, identifies the way to use the produced results. Those theoretical concepts can be practically applied on data set consisting of general information about Erasmus+ projects. Making the exploratory analysis on example set results with common characteristics of projects, while making a deeper, data mining analysis results with uncovering the hidden patterns in data. Less
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:124:534454
Study programmeTitle: Economics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra ekonomije (magistar/magistra ekonomije)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2017-10-06 11:50:39
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