Considering the purpose of this research if is to find out whether it is possible to segment the transactions in such way to allow the easy categorization of whether they represent a ransomware-related transaction or not. Since clustering is an unsupervised classification algorithm, the k clusters do not have a pre-established meaning. In other words, unless there was a perfect segregation of the 29 possible labels, it is impossible to know which cluster.
For evaluating the effectiveness of an attribute on predicting the target feature, I employed two distinct metrics: first, using the R-squared, also known as coefficient of determination \cite{zhang2017coefficient}, and secondly, the accuracy of models when comparing the yielded result against the known value for the training sample. The accuracy is calculated as the inverted error. The error is given by the difference between the expected value (known value for the target feature in the training data) and the resulting value from feeding the model using the training data as input.