We introduce an efficient synchronization model that organizes a 
population of discrete Integrate and Fire oscillators into stable 
and structured groups. Each oscillator fires synchronously with 
all the others within its group, but the groups themselves fire 
with a constant phase difference. The structure of the synchronized 
groups depends on the choice of the coupling function. We show that 
by defining the interaction  between oscillators according to the 
relative distance between them, our model can be used as a general 
clustering algorithm that is simple, efficient, robust, unbiased 
by the size of the clusters, and that can find  an arbitrary number 
of clusters. In addition to helping the model self-organize into 
stable groups,  the synergy between clustering and synchronization 
reduces the computational  complexity significantly. The resulting 
clustering algorithm has several advantages over conventional 
clustering techniques. In particular, it can generate a nested 
sequence of partitions, and  can determine the optimum number of 
clusters in an efficient manner. Moreover, since our approach does 
not involve optimizing an objective function, it is not sensitive to 
initialization, and can incorporate non-metric similarity measures. 
We illustrate the performance of our algorithms with several synthetic 
and real data sets.