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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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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
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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
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