Measuring the Feasibility of Clustering Techniques on Usability Performance Data
Kok Cheng Lim, Ali Selamat, Rose Alinda Alias, Mohd Hazli Mohamed Zabil, Fatimah Puteh, Farhan Mohamed, Universiti Tenaga Nasional Selangor, Malaysia, Universiti Teknologi Malaysia, Johor Bahru, Malaysia, Universiti Teknologi Malaysia, Johor Bahru, Malaysia, Universiti Tenaga Nasional Selangor, Malaysia, Universiti Teknologi Malaysia, Johor Bahru, Malaysia, Universiti Teknologi Malaysia, Johor Bahru, Malaysia · Indian Journal of Science and Technology · 2018
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