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Faktorová analýza dat s ordinálními atributy

  1. Title statementFaktorová analýza dat s ordinálními atributy [rukopis] / Markéta Trnečková
    Additional Variant TitlesFormální konceptuální analýza dat s ordinálními atributy
    Personal name Krmelová, Markéta (dissertant)
    Translated titleFormal concept analysis with ordinal attributes
    Issue data2017
    Phys.des.xii+88 : grafy, tab.
    NoteVed. práce Radim Bělohlávek
    Ved. práce Radim Bělohlávek
    Another responsib. Bělohlávek, Radim, 1971- (thesis advisor)
    Bělohlávek, Radim, 1971- (školitel)
    Another responsib. Univerzita Palackého. Infekční oddělení Prostějov (degree grantor)
    Keywords Matrix decomposition * Factor analysis * Ordinal data * Fuzzy logic
    Form, Genre disertace dissertations
    UDC (043.3)
    CountryČesko
    Languageangličtina
    Document kindPUBLIKAČNÍ ČINNOST
    TitlePh.D.
    Degree programDoktorský
    Degree programInformatika
    Degreee disciplineInformatika
    book

    book

    Kvalifikační práceDownloadedSizedatum zpřístupnění
    00178759-645005956.pdf371.5 MB02.02.2017
    PosudekTyp posudku
    00178759-ved-125112771.pdfPosudek vedoucího
    00178759-opon-122084377.pdfPosudek oponenta
    Průběh obhajobydatum zadánídatum odevzdánídatum obhajobypřidělená hodnocenítyp hodnocení
    00178759-prubeh-983394493.pdf30.09.200802.02.201706.06.2017S2

    The problem of matrix decomposition, also known as matrix factorization problem, is widely investigated in data mining community. Especially Boolean case, where entries of matrices are 0s and 1s. In this thesis we explore the extension of matrix decomposition problem for ordinal data, i.e. data where attributes are values from ordered scales. The replacement of the two-element set of Boolean values and Boolean operations by a multiple-valued set of grades and multiple-valued operations introduced various non-trivial problems. We examine existing algorithms for ordinal data and propose three new algorithms for matrix decomposition problem. We demonstrate that the proposed algorithms deliver decompositions with informative and easy-to-understand factors by analysing real datasets. Moreover, we also compare algorithms presented on synthetic datasets.

Number of the records: 1  

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