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undergraduate thesis
Automatsko grupiranje podataka algoritmom k-means
Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2019. urn:nbn:hr:200:104180
Buršić, Siniša
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Software Engineering
Chair of Programming Languages and Systems

Institutional repository: Faculty of Electrical Engineering, Computer Science and Information Technology Osijek

Cite this document

Buršić, S. (2019). Automatsko grupiranje podataka algoritmom k-means (Undergraduate thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:104180

Buršić, Siniša. "Automatsko grupiranje podataka algoritmom k-means." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2019. https://urn.nsk.hr/urn:nbn:hr:200:104180

Buršić, Siniša. "Automatsko grupiranje podataka algoritmom k-means." Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2019. https://urn.nsk.hr/urn:nbn:hr:200:104180

Buršić, S. (2019). 'Automatsko grupiranje podataka algoritmom k-means', Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 29 April 2024, https://urn.nsk.hr/urn:nbn:hr:200:104180

Buršić S. Automatsko grupiranje podataka algoritmom k-means [Undergraduate thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2019 [cited 2024 April 29] Available at: https://urn.nsk.hr/urn:nbn:hr:200:104180

S. Buršić, "Automatsko grupiranje podataka algoritmom k-means", Undergraduate thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:200:104180

Metadata
TitleAutomatsko grupiranje podataka algoritmom k-means
Title (english)Automatic data clustering with the k-means algorithm
AuthorSiniša Buršić
MentorGoran Martinović (mentor)
MentorDražen Bajer (mentor)
Committee memberGoran Martinović (predsjednik povjerenstva)
Committee memberZdravko Krpić (član povjerenstva)
Committee memberRobert Cupec (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Software Engineering)
(Chair of Programming Languages and Systems)
Osijek
Defense date and country2019-10-01, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Artificial Intelligence
Abstract
U radu je objašnjen pojam grupiranja, navedeni su problemi u klaster analizi od kojih su naglašeni problem samog grupiranja i problem pronalaska optimalnog broja grupa. Navedeni su neke vrste i algoritmi grupiranja od kojih je algoritam k-means mnogo detaljnije objašnjen. Za metodu pronalaska optimalne particije navedeni su relativni indeksi, CH indeks, DB indeks te metoda lakta. Objašnjen je rad programskog rješenja koji koristi algoritam k-means i već navedene indekse. Objašnjen i je naĉin kako koristiti program. Analizirana su rješenja dobivena u programu za šest odabranih skupova.
Abstract (english)
In the final paper the term clustering is explained in detail. Some basic problems of cluster analysis are mentioned, the problem of clustering a set of data and a problem of finding the optimal number of groups for clustering are the two problems that are highlighted. There is also a mention of some types and algorithms for clustering from which k-means algorithm was explained in more detail. CH and DB indices are explained, two methods for finding an optimal partition, as well as elbow method. The way how the realized software solution works as well as how to use it is also explained. Six datasets are selected and are used in the program, solutions given were analyzed.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:104180
Study programmeTitle: Undergraduate University Study Programme in Computer Engineering
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2019-12-20 14:48:26