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Title:Implementacija grafa raztrosa in grafa grupirane matrike v ogrodju NiaARM za vizualizacijo asociacijskih pravil
Authors:ID Bukovnik, Miha (Author)
ID Fister, Iztok (Mentor) More about this mentor... New window
ID Pečnik, Špela (Comentor)
Files:.pdf VS_Bukovnik_Miha_2024.pdf (1,71 MB)
MD5: 3A75F07A48ECBBD4C4E5E6AF1DA8C463
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Podatkovno rudarjenje je ključnega pomena za pridobivanje dragocenih informacij iz velikih podatkovnih zbirk, s široko uporabo na različnih področjih. Rudarjenje asociacijskih pravil odkriva vzorce in povezave v podatkih, kar je izredno uporabno v telekomunikacijah, tržni analizi, upravljanju tveganj ter drugih področjih. Vizualizacija teh pravil z grafom raztrosa in grafom grupirane matrike omogoča boljše razumevanje in analizo pravil. Cilj diplomskega dela je implementacija omenjenih vizualizacijskih tehnik v ogrodju NiaARM, vključno s primerjalno analizo vizualizacij iz paketa arulesViz. Ugotovitve nakazujejo na uporabnost tako NiaARMa kot arulesViza s svojimi specifičnostmi. Medtem ko ogrodje NiaARM omogoča visoko stopnjo interaktivnosti in prilagodljivosti v vizualizaciji, pa paket arulesViz, z večjo podporo skupnosti in dodatnimi viri pomoči, ponuja hitrejše in zanesljivejše vizualizacije.
Keywords:asociacijska pravila, rudarjenje asociacijskih pravil, vizualizacijske tehnike, graf, NiaARM
Place of publishing:Maribor
Publisher:[M. Bukovnik]
Year of publishing:2024
PID:20.500.12556/DKUM-90606 New window
UDC:004.65(043.2)
COBISS.SI-ID:223237891 New window
Publication date in DKUM:22.10.2024
Views:147
Downloads:70
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:10.09.2024

Secondary language

Language:English
Title:Implementation of scatter plot and grouped matrix plot in the NiaARM framework for visualizing association rules
Abstract:Data mining is crucial for extracting valuable information from large datasets, with wide applications across various fields. Association rule mining reveals patterns and connections in data, which is extremely useful in telecommunications, market analysis, risk management and other areas. Visualization of these rules with scatter plots and grouped matrix plots allows for better understanding and analysis of the rules. The aim of this thesis is to implement the mentioned visualization techniques in the NiaARM framework, including a comparative analysis with the arulesViz package. The findings indicate the usefulness of both NiaARM and arulesViz, each with its specific advantages. While the NiaARM framework provides a high level of interactivity and customization in visualizations, the arulesViz package, with greater support from the community and additional resources, offers faster and more reliable visualizations.
Keywords:association rules, association rule mining, visualization methods, graph, NiaARM


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