Multiple learning systems models hold that separate learning systems, often organized around discrepant principles, combine their outputs to support human categorization. Rather than propose a complex model, I adopt a complex systems' viewpoint and propose that multiple learning systems emerge from a flexible and adaptive clustering mechanism's interactions with the environment. The model, CLUSTer Error Reduction (CLUSTER), retains the flexibility characteristic of human learning by building knowledge structures as needed to support a learner's goals. Importantly, CLUSTER can apply ostensibly different procedures to different parts of the stimulus space, a hallmark of multiple systems models. I will describe simulations of human learning studies in which CLUSTER develops different cluster representations for different item types. One of these studies, motivated by CLUSTER, suggests more efficient methods for delivering classroom materials to fifth grade science students. The talk concludes by considering the relation between CLUSTER and findings from the cognitive neuroscience of category learning. CLUSTER is related to a learning circuit in which the hippocampus plays a critical role.