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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

  • 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

    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

  • e-ISSN

  • 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