This paper presents experiments with Predictive Clustering Trees that uncover several subpopulations of the Alzheimer’s disease patients. Our experiments are based on previous research that identified the everyday cognition as one of the most important testing domains in the clinical diagnostic process for the Alzheimer’s disease. We are investigating which biological features have a role in the progression of the disease by observing behavioral response of the patients and their study partners. Our dataset includes 342 male and 317 female patients from the ADNI database that are described with 243 clinical and biological attributes. The resulting clusters, described in terms of biological features, show behavioral and gender specific differences between clusters of patients with progressed disease. These findings suggest a possibility that the Alzheimer’s disease is manifested through different biological pathways.