Measuring the Feasibility of Clustering Techniques on Usability Performance Data

Kok Cheng Lim; Ali Selamat; Rose Alinda Alias; Mohd Hazli Mohamed Zabil; Fatimah Puteh  and Farhan Mohamed

doi:10.17485/ijst/2018/v11i4/121089

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Measuring the Feasibility of Clustering Techniques on Usability Performance Data

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2018/v11i4/121089

Year: 2018, Volume: 11, Issue: 4, Pages: 1-8

Original Article

Measuring the Feasibility of Clustering Techniques on Usability Performance Data

Kok Cheng Lim¹ , Ali Selamat² , Rose Alinda Alias² , Mohd Hazli Mohamed Zabil¹ , Fatimah Puteh² and Farhan Mohamed²

¹ Universiti Tenaga Nasional Selangor, Malaysia; [email protected], [email protected]
² Universiti Teknologi Malaysia, Johor Bahru, Malaysia; [email protected], [email protected], [email protected], [email protected]

^*Author for correspondence
Kok Cheng Lim,
Universiti Tenaga Nasional Selangor, Malaysia; [email protected]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This paper proposes a methodology that utilizes unsupervised machine learning clustering techniques in performance based usability data. This paper will first discuss the introduction and current works in the aforementioned domain followed by proposing the methodology to compare and find a better clustering algorithm in processing usability performance data in the field of mobile augmented reality interfaces. The paper will then present the results yield from an experiment abiding by the proposed methodology and discusses the analysis. The paper will end with a short discussion followed by proposed future works in the research area.

Keywords: Augmented Reality, Clustering, Machine Learning, Mobile, Usability