What is the purpose of the 'train model' step in data mining?
There is a very important difference in Machine Learning (ML) between supervised methods and unsupervised methods:

Supervised learning consists in training a model with some labelled data in order to make the final model able to predict the label on some new (unlabelled) data. This means that the task is designed by choosing exactly what what one wants to predict. For example the task of predicting the author of a text is completely different from predicting the topic of a text, even if the text might be the same.

Note: A &quot;label&quot; is for categorical data, and the task is called classification in this case. The same principle can be applied to numerical values, in which case this is a regression task.


Unsupervised learning consists in detecting patterns in the data with no additional information than the data itself. This means that there's no specific &quot;label&quot; to predict. Very often unsupervised learning is some form of clustering, i.e. grouping instances by similarity. Data mining methods usually belong to this category. In general there is no need for separate training and testing steps with unsupervised methods. However one might need to tune some parameters or sometimes to evaluate the model with some annotated data, so this would require separating training and testing again.