# Super-klust: Another Way of Piecewise Linear Classification

arXiv: https://arxiv.org/abs/2102.01571 · HTML: https://mlsanity.com/paper/2102.01571/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 6 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [Super-k: A Piecewise Linear Classifier Based on Voronoi Tessellations](https://mlsanity.com/paper/2012.15492/) (2020)

## Abstract (arXiv)

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.