Traditional data clustering algorithms are challenged both by conceptual as well as practical issues. This paper will present an approach, bi-clustering, which addresses the presence of dyadic relationships in clustering data. It will also profile the resultant “cluster cells” graphically and with a variety of machine-learning based importance measures.
