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Title:Iskanje skritih informacij v meritvah kvalitete zraka s pomočjo podatkovnega rudarjenja : diplomsko delo
Authors:ID Abeln, Vito (Author)
ID Fister, Iztok (Mentor) More about this mentor... New window
Files:.pdf VS_Abeln_Vito_2023.pdf (2,94 MB)
MD5: 2E0D97177E13046D3AD29B1A851774C5
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Diplomska naloga predstavlja postopek iskanja skritih podatkov iz meritev kvalitete zraka s pomočjo rudarjenja asociativnih pravil. Rudarjenje asociativnih pravil je tehnika, preko katere lahko pridobimo zanimive povezave med podatki iz večjih podatkovnih množic. V zaključnem delu smo prikazali postopek pridobivanja podatkov, obdelavo podatkov, razlago uporabljenih algoritmov in njihovo implementacijo. Opisali smo algoritme Apriori, ECLAT in Fp-growth, ki se uporabljajo pri asociativnem rudarjenju pravil. Predstavili smo tudi numerično rudarjenje asociativnih pravil, pri katerem smo uporabili algoritem optimizacije roja delcev. Rezultati rudarjenja so razkrili različne povezave med vrednostmi meritev, ki smo jih razložili in vizualizirali s pomočjo raznih grafov.
Keywords:rudarjenje asociativnih pravil, kvaliteta zraka, asociativna pravila
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[V. Abeln]
Year of publishing:2023
Number of pages:1 spletni vir (1 datoteka PDF (61 f.))
PID:20.500.12556/DKUM-85965-8192d960-deaf-aebb-7c92-f123ff4f4701 New window
UDC:004.62(043.2)
COBISS.SI-ID:181668355 New window
Publication date in DKUM:03.11.2023
Views:821
Downloads:89
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:22.09.2023

Secondary language

Language:English
Title:Searching for hidden information of air quality measures with data mining
Abstract:This thesis presents a procedure for finding hidden data from air quality measurements using associtation rule mining. Association rule mining is a technique through which interesting associations between data from large datasets can be extracted. In this thesis we show the data mining process, data processing, explanation of the algorithms used and their implementation. We describe the Apriori, ECLAT and Fp-growth algorithms used in associative rule mining. We also presented numerical associative rule mining which used the particle swarm optimisation algorithm. The mining results revealed different relationships between the measurement values, which we explained and visualised using different graphs.
Keywords:association rule mining, air quality, association rules


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