论文标题
K-MS：一种基于形态重建的新型聚类算法
k-MS: A novel clustering algorithm based on morphological reconstruction
论文作者
论文摘要
这项工作提出了一种基于形态重建和启发式方法的聚集算法，称为K-Morphological集合（K-MS）。在最坏的情况下，K-MS比CPU并行K-均值快，并且可以增强数据集的可视化以及非常不同的聚类。它也比对密度和形状（例如有丝分裂和triclust）敏感的类似聚类方法更快。此外，K-MS是确定性的，具有最大簇的内在含义，可以为给定的输入样本和输入参数创建，与K-均值和其他聚类算法不同。换句话说，给定恒定k，一个结构元素和数据集，k-ms会在不使用随机/伪随机函数的情况下产生K或更少的簇。最后，所提出的算法还提供了一种简单的手段，用于消除图像或数据集中的噪声。
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.
