This paper introduces a new scalable approach to clustering based on synchronization of pulse-coupled oscillators. Each data point is represented by an integrate-and-fire oscillator, and the interaction between oscillators is defined according to the relative similarity between the points. The set of oscillators will self-organize into stable phase-locked subgroups. Our approach proceeds by loading only a subset of the data and allowing it to self-organize. Groups of synchronized oscillators are then summarized and purged from memory. We show that our method is robust, scales linearly, and can determine the number of clusters. The proposed approach is empirically evaluated with several synthetic data sets and is used to segment large color images.