Mining Heterogeneous Associations from Pediatric Cancer Data by Relational Concept Analysis

To gain an in-depth understanding of human diseases, biologists typically mine patient data for relevant patterns. Clinical datasets are often unlabeled and involve features, a.k.a. markers, split into classes w.r.t. biological functions, whereby target patterns might well mix both levels. As such heterogeneous patterns are beyond the reach of current analytical tools, dedicated miners, e.g. for association rules, need to be devised. Contemporary multi-relational (MR) association miners, while capable of mixing object types, are rather limited in rule shape (atomic conclusions) while ignoring feature composition. Our own approach builds upon a MR-extension of concept analysis further enhanced with flexible propositionnalisation operators and dedicated MR modeling of patient data. The resulting MR association miner was validated on a pediatric oncology dataset.