Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2017 | Pattern Recognition | k-means clustering,Pattern recognition,Mathematical morphology,Computer science,CUDA,Image segmentation,Heuristics,Unsupervised learning,Structuring element,Artificial intelligence,Cluster analysis,Machine learning |
DocType | Volume | Citations |
Journal | 66 | 7 |
PageRank | References | Authors |
0.48 | 19 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| e o rodrigues | 1 | 27 | 4.50 |
| Leonardo Torok | 2 | 16 | 3.14 |
| Panos Liatsis | 3 | 13 | 3.64 |
| Jose Viterbo | 4 | 20 | 10.34 |
| Aura Conci | 5 | 222 | 32.26 |