  Through its magnetic activity, the Sun governs the conditions in Earth's
vicinity, creating space weather events, which have drastic effects on our
space- and ground-based technology. One of the most important solar magnetic
features creating the space weather is the solar wind, that originates from the
coronal holes (CHs). The identification of the CHs on the Sun as one of the
source regions of the solar wind is therefore crucial to achieve predictive
capabilities. In this study, we used an unsupervised machine learning method,
$k$-means, to pixel-wise cluster the passband images of the Sun taken by the
Atmospheric Imaging Assembly on {\it the Solar Dynamics Observatory} (AIA/SDO)
in 171 \AA, 193 \AA\,, and 211 \AA\,in different combinations. Our results show
that the pixel-wise $k$-means clustering together with systematic pre- and
post-processing steps provides compatible results with those from complex
methods, such as CNNs. More importantly, our study shows that there is a need
for a CH database that a consensus about the CH boundaries are reached by
observers independently. This database then can be used as the "ground truth",
when using a supervised method or just to evaluate the goodness of the models.
