prikaz prve stranice dokumenta Komparativna analiza algoritama grupiranja primjerenih obradi velikih skupova podataka
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master's thesis
Komparativna analiza algoritama grupiranja primjerenih obradi velikih skupova podataka
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:926376
Šmitran, Marko
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Šmitran, M. (2019). Komparativna analiza algoritama grupiranja primjerenih obradi velikih skupova podataka (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:926376

Šmitran, Marko. "Komparativna analiza algoritama grupiranja primjerenih obradi velikih skupova podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:926376

Šmitran, Marko. "Komparativna analiza algoritama grupiranja primjerenih obradi velikih skupova podataka." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:926376

Šmitran, M. (2019). 'Komparativna analiza algoritama grupiranja primjerenih obradi velikih skupova podataka', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 21 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:926376

Šmitran M. Komparativna analiza algoritama grupiranja primjerenih obradi velikih skupova podataka [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 June 21] Available at: https://urn.nsk.hr/urn:nbn:hr:168:926376

M. Šmitran, "Komparativna analiza algoritama grupiranja primjerenih obradi velikih skupova podataka", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:926376

Metadata
TitleKomparativna analiza algoritama grupiranja primjerenih obradi velikih skupova podataka
Title (english)Comparative Analysis Of Clustering Algorithms Applicable To Massive Dataset Processing
AuthorMarko Šmitran
MentorGoran Delač (mentor)
Committee memberGoran Delač (predsjednik povjerenstva)
Committee memberMarin Šilić (član povjerenstva)
Committee memberSiniša Srbljić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-07-09, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Cilj ovog rada je ispitivanje i usporadba svojstava algoritama grupiranja prikladnih za grupiranje velikih skupova podataka. Algoritmi odabrani kao prikladni u tu svrhu su: K-means, CLARANS, BIRCH i DENCLUE. Kvaliteta grupiranja je najvažniji kriterij usporedbe algoritama. Kvaliteta grupiranja mjeri se unutarnjim i vanjskim validacijskim metrika. Objašnjen je rad svih odabranih algoritama i validacijske metrike su definirane, a potom su provedena ispitivanja. Algoritmi grupiranja primjenjeni su ponajprije nad sintetiˇckim skupovima podataka, ali i na dva stvarna skupa. Ispitan je utjecaj oblika grupa, neravnoteže med¯u grupama i šuma u podacima na kvalitete grupiranja te skalabilnost algoritama. Algoritmi su primjenjeni i na stvarnim skupovima te je ocijenjena njihova prikladnost.
Abstract (english)
The goal of this paper is testing and comparison of properties of clustering algorithms applicable to massive dataset processing. Algorithms chosen as applicable for this task are: K-means, CLARANS, BIRCH and DENCLUE. Quality of clustering is the most important criterion of comparison between algorithms. Clustering quality is measured by internal and external validation metrics. Each chosen clustering algorithm is explained and each used validation metrics are defined, then testing was conducted. Clustering algorithms were primarily used on syntetic datasets, but also on two real ones. The goal of the conducted tests was to discern sensitivity of clustering algorithms to the shape of clusters, cluster imbalance and clustering of noisy data and their impact on quality of clusterings. Scalability of clustering algorithms was also tested. Algorithms were also used on real datasets and their applicability was discussed.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:926376
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2020-01-07 22:27:30