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Title:ANALIZA METOD PODATKOVNEGA RUDARJENJA NA PRIMERU IZ KINEZIOLOGIJE
Authors:ID Plevčak, Tadej (Author)
ID Podgorelec, Vili (Mentor) More about this mentor... New window
Files:.pdf UNI_Plevcak_Tadej_2012.pdf (4,71 MB)
MD5: A77F5954019A7DEDA1206579B332930C
PID: 20.500.12556/dkum/34d60a1e-05c4-4d41-b38a-861dc7e90dbe
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Naše diplomsko delo predstavlja teoretično osnovo podatkovnega rudarjenja in prikaz na praktičnem primeru. V prvem, teoretičnem delu smo predstavili osnovne koncepte podatkovnega rudarjenja, metode in orodja za učenje in pridobivanje novega znanja ter metode za primerjavo uspešnosti različnih klasifikacijskih modelov. V drugem, praktičnem delu smo najprej analizirali podatkovno množico iz področja kineziologije in poiskali najboljši klasifikacijski model, ki iz podatkov o mišicah (merjenih z metodo TMG) pove kako uspešna je oseba pri nekih športnih preizkusih. Nato smo uporabili najboljše modele (klasifikatorje) za izdelavo aplikacije, s katero je mogoča klasifikacija novih primerkov po opravljenih kinezioloških meritvah.
Keywords:podatkovno rudarjenje, strojno učenje, klasifikacija, Weka, kineziologija
Place of publishing:Maribor
Publisher:[T. Plevčak]
Year of publishing:2012
PID:20.500.12556/DKUM-37786 New window
UDC:004.62:004.89(043.2)
COBISS.SI-ID:16622358 New window
NUK URN:URN:SI:UM:DK:STPMAEKQ
Publication date in DKUM:18.12.2012
Views:2139
Downloads:280
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:ANALYSIS OF DATA MINING METHODS APPLIED TO KINESIOLOGY
Abstract:Our thesis presents theoretical background of data mining and an example of application to real-life data. We introduce basic concepts, methods and tools for performing data mining and knowledge discovery, and some metrics for evaluating classification models. Afterwards we demonstrate data mining using real-life data set containing parameters describing muscle performance in relation to sport activities. Our goal is to find the most successful classification algorithm for each sport activity. Classifiers with good performance are then embedded in a computer programme that is capable of classifying new instances and thus predicting how well someone might perform in certain sports.
Keywords:data mining, machine learning, classification, Weka, kinesiology


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