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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Frontiers in Artificial Intelligence and Applications

ISBN

978-1-64368-114-6

ISSN

0922-6389

e-ISSN

—

Počet stran výsledku

13

Strana od-do

317-329

Název nakladatele

IOS Press BV

Místo vydání

Amsterdam

Místo konání akce

Japonsko

Datum konání akce

22. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—