Super-klust: Another Way of Piecewise Linear Classification

TL;DR

Super-klust is a simplified piecewise-linear classification method that uses k-means clustering and Voronoi tessellations, achieving similar performance to the more complex Super-k algorithm.

Contribution

The paper introduces Super-klust, a new piecewise-linear classifier based on Voronoi tessellations and k-means clustering, simplifying the previous Super-k approach.

Findings

Super-klust performs comparably to Super-k in experiments.

Replacing voxelization with k-means simplifies the algorithm.

Super-klust effectively covers data with labeled Voronoi tessellations.

Abstract

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.