  We employ unsupervised machine learning to enhance the accuracy of our
recently presented scaling method for wave confinement analysis [1]. We employ
the standard k-means++ algorithm as well as our own model-based algorithm. We
investigate cluster validity indices as a means to find the correct number of
confinement dimensionalities to be used as an input to the clustering
algorithms. Subsequently, we analyze the performance of the two clustering
algorithms when compared to the direct application of the scaling method
without clustering. We find that the clustering approach provides more
physically meaningful results, but may struggle with identifying the correct
set of confinement dimensionalities. We conclude that the most accurate outcome
is obtained by first applying the direct scaling to find the correct set of
confinement dimensionalities and subsequently employing clustering to refine
the results. Moreover, our model-based algorithm outperforms the standard
k-means++ clustering.
