Abstract: A method for providing a balanced training dataset for training a Machine Learning model includes receiving a multi-class dataset containing data records of at least one majority class and at least one minority class, representing the data records of the at least one majority class by using at least one content-based representation of the data records, k-means clustering of the data records of the at least one majority class based on the at least one content-based representation, selecting the data record closest to the centroid of each cluster as representative of the respective cluster, aggregating the selected data records of the at least one majority class and the data records of the at least one minority class, and providing the aggregated data records as training dataset. Also disclosed is a data processing device, a computer program product, a data carrier signal, and a method for selecting representatives of data records.