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DKUM
EPF - Faculty of Business and Economics
FE - Faculty of Energy Technology
FERI - Faculty of Electrical Engineering and Computer Science
FF - Faculty of Arts
FGPA - Faculty of Civil Engineering, Transportation Engineering and Architecture
FKBV - Faculty of Agriculture and Life Sciences
FKKT - Faculty of Chemistry and Chemical Engineering
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FS - Faculty of Mechanical Engineering
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FZV - Faculty of Health Sciences
MF - Faculty of Medicine
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UM - University of Maribor
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Fakulteta za kmetijstvo
Fakulteta za logistiko
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Title:
Orodje za vizualno analitiko večdimenzionalnih podatkov
Authors:
Brumen, Matej
(Author)
Mongus, Domen
(Mentor)
More about this mentor...
Lukač, Niko
(Co-mentor)
Files:
MAG_Brumen_Matej_2017.pdf
(13,82 MB)
Language:
Slovenian
Work type:
Master's thesis/paper (mb22)
Typology:
2.09 - Master's Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V magistrskem delu predstavimo orodje za vizualno analitiko večdimenzionalnih podatkov. V analizi sorodnega dela predstavimo tehnike vizualizacije masovnih podatkov in tradicionalne ter napredne tehnike odkrivanja znanja. Na tej osnovi podrobneje opišemo razvito orodje in s primeri uporabe demonstriramo njegovo učinkovitost. Z rezultati pa tudi potrdimo pravilnost delovanja implementiranih funkcionalnosti.
Keywords:
vizualna analitika
,
masovni podatki
,
analiza odvisnosti
,
korelacija
,
odkrivanje znanja
Year of publishing:
2017
Publisher:
M. Brumen
Source:
[Maribor
UDC:
004.515.66:004.62(043.2)
COBISS_ID:
20971798
NUK URN:
URN:SI:UM:DK:RJCHXSNV
License:
This work is available under this license:
Creative Commons Attribution Non-Commercial No Derivatives 4.0 International
Views:
429
Downloads:
62
Metadata:
Categories:
KTFMB - FERI
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Secondary language
Language:
English
Title:
A tool for visual analytics of multidimensional data
Abstract:
This Master's thesis introduces a tool for visual analytics of multidimensional data. During the analysis of the related work, technique for big data visualization and traditional as well as advanced knowledge discovery methods are presented. On this basis, the developed tool is described details, while its efficiency is demonstrated with several use-cases. The correctness of the developed methods is proved with the results.
Keywords:
visual analytics
,
big data
,
correlation analysis
,
knowledge discovery
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