Comparing the Accuracy of Hierarchical Agglomerative and K-means Clustering on Mobile Augmented Reality Usability Metrics
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F19%3A50016631" target="_blank" >RIV/62690094:18450/19:50016631 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICBDA47563.2019.8987044" target="_blank" >http://dx.doi.org/10.1109/ICBDA47563.2019.8987044</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICBDA47563.2019.8987044" target="_blank" >10.1109/ICBDA47563.2019.8987044</a>
Alternative languages
Result language
angličtina
Original language name
Comparing the Accuracy of Hierarchical Agglomerative and K-means Clustering on Mobile Augmented Reality Usability Metrics
Original language description
This article presents the experimental work of comparing the performances of two machine learning approaches, namely Hierarchical Agglomerative Clustering and K-means Clustering on Mobile Augmented Reality Usability datasets. The datasets comprises of 2 separate categories of data, namely performance and self-reported, which are completely different in nature, techniques and affiliated biases. This research will first present the background and related literature before presenting initial findings of identified problems and objectives. This paper will the present in detail the proposed methodology before presenting the evidences and discussion of comparing this two widely used machine learning approach on usability data.
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
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2019 IEEE Conference on Big Data and Analytics, ICBDA 2019
ISBN
978-1-72813-308-9
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
34-40
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
US, piscataway
Event location
Penang, Malaysia
Event date
Nov 19, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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