Radio interferometry usually compensates for high levels of noise in sensor/antenna electronics by throwing data and energy at the problem: observe longer, then store and process it all. We propose instead a method to remove the noise explicitly before imaging. To this end, we developed an algorithm that first decomposes the instances of antenna correlation matrix, the so-called visibility matrix, into additive components using Singular Spectrum Analysis and then cluster these components using graph Laplacian matrix. We show through simulation the potential for radio astronomy, in particular, illustrating the benefit for LOFAR, the low frequency array in Netherlands. Least-squares images are estimated with far higher accuracy with low computation cost without the need for long observation time.