稀疏位置拓扑图聚类
Topological graph clustering with thin position
Oklahoma State University（俄克拉荷马州立大学）
摘要：
聚类算法将一组数据点划分为更小的集合（聚类），使得每个子集比整体更紧密地打包。许多聚类方法将向量数据转换为具有反映点上的距离或相似性度量的边的图，然后寻找高度连通的子图。我们引入这样一个算法的基础上借用的思想，从拓扑概念的薄的位置为节点和三维流形。
英文摘要：
A clustering algorithm partitions a set of data points into smaller sets (clusters) such that each subset is more tightly packed than the whole. Many approaches to clustering translate the vector data into a graph with edges reflecting a distance or similarity metric on the points, then look for highly connected subgraphs. We introduce such an algorithm based on ideas borrowed from the topological notion of thin position for knots and 3-dimensional manifolds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
