Suppose that you have a very long list of string sequences, such as a list of amino acid structures ('PHE-SER-CYS', 'GLN-ARG-SER',…), product serial numbers ('AB121E', 'AB323', 'DN176'…), or users UIDs, and you are required to create a validation process of some kind that will detect anomalies in this sequence. An anomaly might be a string that follows a slightly different or unusual format than the others (whether it was created by mistake or on purpose) or just one that is extremely rare. To make things even more interesting, suppose that you don't know what is the correct format or structure that sequences suppose to follow. This is a relatively common problem (though with an uncommon twist) that many data scientists usually approach using one of the popular unsupervised ML algorithms, such as DBScan, Isolation Forest, etc. Many of these algorithms typically do a good job in finding anomalies or outliers by singling out data points that are relatively far from the others or from areas in which most data points lie.