Automatic determination of the number of clusters using spectral algorithms
Guido Sanguinetti, Jonathan Laidler and Neil Lawrence
In: IEEE Machine Learning for Signal Processing 2005, 28-30 Sept 2005, Mystic, Connecticut, USA.
We introduce a novel spectral clustering algorithm that allows us to
automatically determine the number of clusters in a dataset. The algorithm
is based on a theoretical analysis of the spectral properties of block
diagonal affinity matrices; in contrast to established methods, we
do not normalise the rows of the matrix of eigenvectors, and argue
that the non-normalised data contains key information that allows
the automatic determination of the number of clusters present. We
present several examples of datasets successfully clustered by our
algorithm, both artificial and real, obtaining good results
even without employing refined feature extraction techniques.