  Minimum spanning trees (MSTs) provide a convenient representation of datasets
in numerous pattern recognition activities. Moreover, they are relatively fast
to compute. In this paper, we quantify the extent to which they are meaningful
in low-dimensional partitional data clustering tasks. By identifying the upper
bounds for the agreement between the best (oracle) algorithm and the expert
labels from a large battery of benchmark data, we discover that MST methods can
be very competitive. Next, we review, study, extend, and generalise a few
existing, state-of-the-art MST-based partitioning schemes. This leads to some
new noteworthy approaches. Overall, the Genie and the information-theoretic
methods often outperform the non-MST algorithms such as K-means, Gaussian
mixtures, spectral clustering, Birch, density-based, and classical hierarchical
agglomerative procedures. Nevertheless, we identify that there is still some
room for improvement, and thus the development of novel algorithms is
encouraged.
