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. This paper contributes in presenting evidences showing K-means as the better performing clustering algorithm when compared to Hierarchical Agglomerative when implemented on the usability datasets. The results shown has contradicted with some recent studies claiming otherwise, and the findings have created more research gaps pertaining the combined utilization of machine learning and usability analysis.
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