  With our previous study, the Super-k algorithm, we have introduced a novel
way of piecewise-linear classification. While working on the Super-k algorithm,
we have found that there is a similar, and simpler way to explain for obtaining
a piecewise-linear classifier based on Voronoi tessellations. Replacing the
multidimensional voxelization and expectation-maximization stages of the
algorithm with a distance-based clustering algorithm, preferably k-means, works
as well as the prior approach. Since we are replacing the voxelization with the
clustering, we have found it meaningful to name the modified algorithm, with
respect to Super-k, as Supervised k Clusters or in short Super-klust. Similar
to the Super-k algorithm, the Super-klust algorithm covers data with a labeled
Voronoi tessellation, and uses resulting tessellation for classification.
According to the experimental results, the Super-klust algorithm has similar
performance characteristics with the Super-k algorithm.
