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undergraduate thesis
Usporedba metoda grupiranja primjenom programskog jezika Python
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:676899
Bjelčić, Tomislav
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Bjelčić, T. (2021). Usporedba metoda grupiranja primjenom programskog jezika Python (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:676899

Bjelčić, Tomislav. "Usporedba metoda grupiranja primjenom programskog jezika Python." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:676899

Bjelčić, Tomislav. "Usporedba metoda grupiranja primjenom programskog jezika Python." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:676899

Bjelčić, T. (2021). 'Usporedba metoda grupiranja primjenom programskog jezika Python', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:676899

Bjelčić T. Usporedba metoda grupiranja primjenom programskog jezika Python [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 November 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:676899

T. Bjelčić, "Usporedba metoda grupiranja primjenom programskog jezika Python", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:676899

Metadata
TitleUsporedba metoda grupiranja primjenom programskog jezika Python
Title (english)Comparison of Clustering Methods Using Python Programming Language
AuthorTomislav Bjelčić
MentorGoran Delač (mentor)
Committee memberGoran Delač (predsjednik povjerenstva)
Committee memberAdrian Satja Kurdija (član povjerenstva)
Committee memberLucija Šikić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-09-13, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Grupiranje je postupak particioniranja skupa neoznačenih podatka na podskupove sa međusobno sličnim podacima. Ovaj rad opisuje osnovne pojmove problema grupiranja i uspoređuje četiri različita algoritama grupiranja na jednostavnim skupovima podataka. Algoritam K-srednjih vrijednosti je najpoznatiji, najučinkovitiji algoritam grupiranja koji se zasniva na modelu centroida. Zahtjeva poznavanje broja grupa i može grupirati konveksne oblike grupa. Hijerarhijsko aglomerativno grupiranje zasniva se na kriteriju spajanja, gradi hijerarhiju grupa spajanjem i nije učinkovit kao K-sredina ali je primjenjiv na svim vrstama podataka. DBSCAN se zasniva na modelu gustoće točaka koje prepoznaje i grupira gusto raspoređene podatke. DBSCAN je brz, poznaje koncept šuma i može grupirati proizvoljne oblike. EM algoritam Gaussovih mješavina nastoji procijeniti parametre modela Gaussovih mješavina koji podatke promatra kao realizacije više Gaussovih razdioba. EM algoritam je brz i može grupirati konveksne grupe raznih oblika. Opisani algoritmi su pokrenuti, vrednovani i uspoređeni na 6 različita, relativno jednostavna skupa dvodimenzijskih podataka sa realnim značajkama.
Abstract (english)
Clustering is a process of partitioning a set of unlabeled data into subsets with similar data. This thesis describes basic concepts regarding cluster analysis and compares four different clustering algorithms on simple data sets. K-means algorithm is the best known, most efficient clustering algorithm based on the centroid model. K-means requires knowledge of the number of clusters and is able to group convex group shapes. Hierarchical agglomerative clustering is based on linkage criterion, builds a hierarchy of clusters by merging them, and is not as efficient as K-means, but is applicable to all types of data. DBSCAN is based on the density model which discovers and clusters dense regions of data. DBSCAN is fast, has a notion of noise and is able to cluster arbitrarily shaped clusters. EM algorithm for Gaussian mixture model aims to estimate parameters of Gaussian mixture model that views the data as realizations of several Gaussian distributions. EM algorithm is fast and is able to cluster convex groups of various shapes. Described algorithms were executed, evaluated and compared on 6 different, relatively simple sets of two-dimensional data with real number features.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:676899
Study programmeTitle: Computing
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)
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Created on2023-01-14 20:05:35