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Title:ANALIZA IN PRIMERJAVA PLATFORM ZA PODATKOVNO RUDARJENJE RAPIDMINER IN WEKA
Authors:ID Miloševič, Aleksej (Author)
ID Podgorelec, Vili (Mentor) More about this mentor... New window
ID Karakatič, Sašo (Comentor)
Files:.pdf UN_Milosevic_Aleksej_2016.pdf (2,22 MB)
MD5: 0744BCFA7DA7EDAE61F60817F550DC51
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V pričujočem diplomskem delu sta analizirani in primerjani splošnonamenski platformi za podatkovno rudarjenje RapidMiner in Weka. V uvodnem delu diplomskega dela so razložene osnove strojnega učenja in podatkovnega rudarjenja ter podrobneje definirane metode dela, ki so uporabljene v praktičnem delu. Primerjava je razdeljena na teoretični in eksperimentalni del. V teoretičnem delu so na podlagi definirane metodologije identificirane pomembne lastnosti orodij in primerjane med seboj, v eksperimentalnem delu pa sta primerjani točnost in F-Mera implementacij algoritmov k-najbližjih sosedov, Naključni gozdovi in Naivni Bayes. S pomočjo statističnih testov je bilo ugotovljeno, da se nobena izvedenka algoritma od drugega statistično pomembno ne razlikuje.
Keywords:strojno učenje, klasifikacija, primerjava, RapidMiner, Weka
Place of publishing:[Maribor
Publisher:A. Miloševič
Year of publishing:2016
PID:20.500.12556/DKUM-62086 New window
UDC:004.65(043.2)
COBISS.SI-ID:19991062 New window
NUK URN:URN:SI:UM:DK:JGSVWTSO
Publication date in DKUM:16.09.2016
Views:2179
Downloads:271
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:ANALYSIS AND COMPARISON OF DATA MINING PLATFORMS RAPIDMINER AND WEKA
Abstract:The following thesis analyses and compares two general-purpose platforms for data mining, RapidMiner and Weka. The introductory part of this diploma thesis describes the basics of machine learning and data mining as well as the specifically defined work methods, which are used in the experimental part. The comparison is divided into the theoretical and the empirical part. In the theoretical part the important characteristics of the tools are identified and compared on the basis of the defined methodology, whereas in the empirical part the accuracy and the F-measure of implementations of the algorithms K Nearest Neighbor, Random Forest and Naive Bayes are compared. Using appropriate statistical tests, it was found that no version of the algorithm significantly differs from another.
Keywords:machine learning, classification, comparison, RapidMiner, Weka


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