k-MS: A novel clustering algorithm based on morphological reconstruction
This work addresses clustering challenges for data analysis by offering a deterministic and efficient alternative to existing methods, though it appears incremental as it builds on morphological techniques.
The authors tackled the problem of clustering by introducing k-MS, a deterministic algorithm based on morphological reconstruction that is faster than CPU-parallel k-Means in worst cases and outperforms density- and shape-sensitive methods like Mitosis and TRICLUST, while producing enhanced visualizations and distinct clusters.
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.