Résumé
Upon the introduction of novel medical therapies, an array of semantically different data is gathered from the participant’s cohort. Unsupervised learning is always privileged as a preliminary step for data investigation, to extract valuable information before embarking on the tedious task of data labeling. Clustering is one of the techniques that provide a comprehensive overview for exploratory data analysis, aiding in the identification of patient communities. With OptClust4Rec, we provide a characterization of clusters, from which we can derive recommendations for patients undergoing therapy treatment. Our focus is on optimizing the clustering and the dimensionality reduction based on concise metrics and data topology analysis.