1. A method of clustering a plurality of information items using nonnegative tensor factorization, the method comprising: receiving, by a processing device, one or more class labels, wherein each class label corresponds to an information item; receiving, by the processing device, a selection for a nonnegative tensor factorization model having an associated objective function; receiving, by the processing device, one or more parameter values, wherein each parameter value corresponds to one of one or more penalty constraints; determining, by the processing device, a constrained objective function including the one or more penalty constraints, wherein the constrained objective function is based on the objective function associated with the selected nonnegative tensor factorization model, the one or more parameter values and the one or more class labels; and determining, by the processing device, clusters for the plurality of information items by evaluating the constrained objective function.