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Title:Primerjava algoritmov Apriori in GSP nad podatki Covid-19 z osredotočenjem na časovno dimenzijo : diplomsko delo
Authors:ID Kulčar, Vita (Author)
ID Fister, Iztok (Mentor) More about this mentor... New window
ID Novak, Damijan (Comentor)
Files:.pdf VS_Kulcar_Vita_2025.pdf (2,46 MB)
MD5: 5FC597073906ED2FFA6FF5398082A401
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Diplomsko delo se osredotoča na uporabo metod podatkovnega rudarjenja, specifično, na odkrivanje asociativnih pravil. V uvodu so predstavljeni osnovni pojmi in tehnike rudarjenja, vključno z merami zanimivosti, ki se uporabljajo za analizo povezanosti med različnimi elementi v podatkih. Delo podrobno opisuje algoritem Apriori in algoritem posplošenih zaporednih vzorcev, njune prednosti in slabosti ter implementacijo za generiranje pogostih vzorcev in oblikovanje pravil. Osredotoča se tudi na analizo podatkov z upoštevanjem časovne dimenzije in preučevanje rezultatov rudarjenja, vključno z vizualizacijo in interpretacijo ugotovitev.
Keywords:algoritem Apriori, algoritem GSP, asociativna pravila, rudarjenje velepodatkov
Publication status:Published
Publication version:Version of Record
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[V. Kulčar]
Year of publishing:2025
Number of pages:1 spletni vir (1 datoteka PDF (X, 44 str.))
PID:20.500.12556/DKUM-91932 New window
UDC:004.6.021(043.2)
COBISS.SI-ID:236609795 New window
Publication date in DKUM:08.05.2025
Views:115
Downloads:39
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-SA 4.0, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Link:http://creativecommons.org/licenses/by-nc-sa/4.0/
Description:A Creative Commons license that bans commercial use and requires the user to release any modified works under this license.
Licensing start date:03.03.2025

Secondary language

Language:English
Title:Comparison of the Apriori and GSP algorithms over Covid-19 data with a focus on the temporal dimension
Abstract:This thesis focuses on the application of data mining methods, specifically on the discovery of association rules. In the introduction, the basic concepts and techniques of data mining are introduced, including the metrics used to analyse the association between different elements in the data. The work describes in detail the Apriori algorithm and the Generalized Sequential Pattern (GSP) algorithm, their strengths and weaknesses, and their implementation for frequent pattern generation and rule generation. It also focuses on time-domain data analysis and the study of mining results, including visualisation and interpretation of findings.
Keywords:Apriori algorithm, GSP algorithm, association rules, big data mining


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