k-MS: A novel clustering algorithm based on morphological reconstruction

TL;DR

k-MS is a new clustering algorithm based on morphological reconstruction that is faster, deterministic, and produces clearer, more distinct clusters compared to traditional methods like k-Means, especially in complex datasets.

Contribution

The paper introduces k-MS, a novel clustering algorithm that improves speed, determinism, and visualization quality over existing methods, with intrinsic control over the maximum number of clusters.

Findings

k-MS outperforms k-Means in speed in worst-case scenarios.

k-MS produces more distinct and visually enhanced clusters.

k-MS can effectively remove noise from datasets and images.

Abstract

This work proposes a clusterization algorithm called k-Morphological Sets (k-MS), based on morphological reconstruction and heuristics. k-MS is faster than the CPU-parallel k-Means in worst case scenarios and produces enhanced visualizations of the dataset as well as very distinct clusterizations. It is also faster than similar clusterization methods that are sensitive to density and shapes such as Mitosis and TRICLUST. In addition, k-MS is deterministic and has an intrinsic sense of maximal clusters that can be created for a given input sample and input parameters, differing from k-Means and other clusterization algorithms. In other words, given a constant k, a structuring element and a dataset, k-MS produces k or less clusters without using random/ pseudo-random functions. Finally, the proposed algorithm also provides a straightforward means for removing noise from images or datasets in general.