  We present a graph-theoretical approach to data clustering, which combines
the creation of a graph from the data with Markov Stability, a multiscale
community detection framework. We show how the multiscale capabilities of the
method allow the estimation of the number of clusters, as well as alleviating
the sensitivity to the parameters in graph construction. We use both synthetic
and benchmark real datasets to compare and evaluate several graph construction
methods and clustering algorithms, and show that multiscale graph-based
clustering achieves improved performance compared to popular clustering methods
without the need to set externally the number of clusters.
