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

PATTERN RECOGNITION

卷:66

Article

Rodrigues, Erick Oliveira¹  Torok, Leonardo¹  Liatsis, Panos²  Viterbo, Jose¹  Conci, Aura¹ 

[1] Univ Fed Fluminense, Dept Comp Sci, Niteroi, RJ, Brazil

[2] Petr Inst, Dept Elect Engn, Abu Dhabi, U Arab Emirates

关键词: K-Means;    Morphological reconstruction;    Mathematical morphology;    GPU;    CUDA;    Machine learning;    Unsupervised learning;    Image segmentation;    Noise removal;    Clusterization;    Clustering;    Parallelism;   

DOI  :  10.1016/j.patcog.2016.12.027

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free