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A comparative study of major clustering techniques for MAR learning usability prioritization processes

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50017206" target="_blank" >RIV/62690094:18450/20:50017206 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.3233/FAIA200577" target="_blank" >http://dx.doi.org/10.3233/FAIA200577</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3233/FAIA200577" target="_blank" >10.3233/FAIA200577</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    A comparative study of major clustering techniques for MAR learning usability prioritization processes

  • Original language description

    This paper presents and discusses a comparative study of three major clustering categories namely Hierarchical-based, Iterative mode-based and Partition-based in analyzing and prioritizing Mobile Augmented reality (MAR) Learning (MAR-learning) usability data. This paper first discusses the related works in usability and clustering before moving on to the identification of gaps that can be addressed through experimentation. This paper will then propose a research methodology to measure four common clustering techniques on MAR-learning usability data. The paper will then discourse comparative results showing how Mini-batch K-means to be an ideal technique within the experimental setup. The paper will then present important research highlights, discussion, conclusion and future works. © 2020 The authors and IOS Press. All rights reserved.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Frontiers in Artificial Intelligence and Applications

  • ISBN

    978-1-64368-114-6

  • ISSN

    0922-6389

  • e-ISSN

  • Number of pages

    13

  • Pages from-to

    317-329

  • Publisher name

    IOS Press BV

  • Place of publication

    Amsterdam

  • Event location

    Japonsko

  • Event date

    Oct 22, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article