We examine the problem of adaptation and learning over networks with selfish agents. In order to motivate agents to cooperate, we allow the agents to select their partners according to whether they can help them reduce their utility costs. We divide the operation of the network into two stages: a cluster formation stage and an information sharing stage. During cluster formation, agents evaluate a long-term combined cost function and decide on whether to cooperate or not with other agents. During the subsequent information sharing phase, agents share and process information over their sub-networks. Simulations illustrate how the clustering technique enhances the mean-square-error performance of the agents over non-cooperative processing.