Automatic labelling of clusters of discrete and continuous data with supervised machine learning

Highlights：

• This study presents a definition of the labelling problem and a solution that is based on techniques for supervised learning, unsupervised learning and a discretisation model.

• A method with unsupervised learning is applied to the clustering problem, and a supervised learning algorithm will detect the relevant attributes to define each formed cluster.

• Some strategies are used to form a methodology that presents a label (based on attributes and values) for each provided cluster.

• Discretisation methods 226 will be used to determine the ranges of values of the attributes presented in the 227 labels.

• This methodology is applied to three different databases, in which acceptable results were achieved with an average that exceeds 92.89% of correctly labelled elements.