  Machine learning techniques have been shown to be effective to recognize
different phases of matter and produce phase diagrams in the parameter space
interested, while they usually require prior labeled data to perform well.
Here, we propose a machine learning procedure, mainly in an unsupervised
manner, which can first identify topological/non-topological phases and then
refine the locations of phase boundaries. By following this proposed procedure,
we expand our previous work on the one-dimensional $p$-wave superconductor
[Phys. Rev. B 102, 054512 (2020)] and further on the Su-Schrieffer-Heeger
model, with an emphasis on using the quantum entanglement-based quantities as
the input features. We find that our method not only reproduces similar results
to the previous work with sharp phase boundaries but importantly it also does
not rely on prior knowledge of the phase space, e.g., the number of phases
present. We conclude with a few remarks about its potential, limitations, and
explainabilities.
