论文标题
通过构建超平面聚类
Clustering by Constructing Hyper-Planes
论文作者
论文摘要
作为一种基本的机器学习方法，将算法组数据指数基于它们的相似性或分布而分为不同的类别。我们通过查找超平台以区分数据点来提出聚类算法。它依靠点之间的边际空间。然后，我们将这些超平台结合在一起，以确定集群的中心和数量。由于算法基于线性结构，因此它可以准确，灵活地近似数据集的分布。为了评估其性能，我们通过在不同类型的基准数据集上进行实验，将其与一些著名的聚类算法进行了比较。它的表现明显优于其他方法。
As a kind of basic machine learning method, clustering algorithms group data points into different categories based on their similarity or distribution. We present a clustering algorithm by finding hyper-planes to distinguish the data points. It relies on the marginal space between the points. Then we combine these hyper-planes to determine centers and numbers of clusters. Because the algorithm is based on linear structures, it can approximate the distribution of datasets accurately and flexibly. To evaluate its performance, we compared it with some famous clustering algorithms by carrying experiments on different kinds of benchmark datasets. It outperforms other methods clearly.
