  We present a new clustering algorithm that is based on searching for natural
gaps in the components of the lowest energy eigenvectors of the Laplacian of a
graph. In comparing the performance of the proposed method with a set of other
popular methods (KMEANS, spectral-KMEANS, and an agglomerative method) in the
context of the Lancichinetti-Fortunato-Radicchi (LFR) Benchmark for undirected
weighted overlapping networks, we find that the new method outperforms the
other spectral methods considered in certain parameter regimes. Finally, in an
application to climate data involving one of the most important modes of
interannual climate variability, the El Nino Southern Oscillation phenomenon,
we demonstrate the ability of the new algorithm to readily identify different
flavors of the phenomenon.
