*2.1. Description of Unsupervised Machine Learning Algorithms*

Current machine learning techniques mainly fall into two groups: supervised and unsupervised learning [41]. The UMLA is a self-organization method to find patterns in unlabeled data. Cluster analysis is, a subset of UMLA methods, and in general, is based on the principle of grouping similar observations and segmenting dissimilar observations [42]. Anomalous data points that differ from others may then be filtered [43]. A large number of clustering algorithms exist, including K-means, Affinity Propagation, and Mean Shift. In this research, we employed the SCI, CHI, and DBI to assess the performance of the cluster, because of their accuracy and wide applicability in a similar type of studies [44–46].
