In this paper we investigate the generation of phenotypes for kidney transplant donors and recipients to assist with decision making around organ allocation. We present an ensemble clustering approach for multi-type data (numerical and categorical) using two different clustering approaches—i.e., model based and vector quantization based clustering. These clustering approaches were applied to a large, US national deceased donor kidney transplant recipient database to characterize members of each cluster (in an unsupervised fashion) and to determine whether the subsequent risk of graft failure differed for each cluster. We generated three distinct clusters of recipients, which were subsequently used to generate phenotypes. Each cluster phenotype had recipients with varying clinical features, and the risk of kidney transplant graft failure and mortality differed across clusters. Importantly, the clustering results by both approaches demonstrated a significant overlap. Utilization of two distinct clustering approaches may be a novel way to validate unsupervised clustering techniques and clustering can be used for organ allocation decision making on the basis of differential outcomes.