We use techniques from Kleinberg's Hubs and Authorities and kernel functions as in Support Vector Machines to define a new form of clustering. The increase in the degree of non linearity of the kernels leads to an increase in the granularity of the data space and to a natural evolution of clusters into subclusters. The algorithm proposed to construct zoomed clusters has been designed to run on very large data sets as found in web directories and bioinformatics.