发表机构
São Paulo State University (UNESP)（圣保罗州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出往返KNN聚类方法，在有向最近邻图上多尺度检测层次簇，无需预设簇数，经细化提升聚类精度，在合成数据上表现优异。
AI 中文摘要
我们提出了往返KNN聚类（RTKNNC），一种基于图的方法，用于在多个邻域尺度上发现簇结构，而无需预先指定簇的数量。与先使k近邻（KNN）图无向化的方法不同，RTKNNC保留邻居关系的两个方向：给定点选择哪些点，以及哪些点选择该点。入向选择被视为加权投票，帮助决定在递归的前向-反向遍历过程中哪些局部连接保持可见。随着K的增加重复该过程，揭示出随着邻域尺度增长，群组如何持续存在或合并；对于结构细化之前的参考逆平方模型，簇可以合并但不能分裂。由于图连通性偶尔会通过稀疏桥或小的重叠区域连接不同的群组，我们添加了一个可选的无需标签的细化步骤。它首先测试已形成的组件是否更好地由两个或三个高斯子总体描述，并且仅当提议的群组足够大且与可见的KNN图一致时才接受细分。在八个合成数据集和K=2,…,16的情况下，独立的C和Python实现在所有120次参考运行中产生了相同的分区。细化将可变密度基准上的调整兰德指数从0.7817提高到0.9627，在稀疏桥基准上从0.8083提高到0.9853。与七种外部聚类方法的比较显示，在保持无标签簇构建过程的同时，性能具有竞争力。
英文摘要
We introduce Round-Trip KNN Clustering (RTKNNC), a graph-based method for finding cluster structure at several neighbourhood scales without requiring the number of clusters in advance. Unlike approaches that first make a $k$-nearest-neighbour (KNN) graph undirected, RTKNNC keeps both directions of the neighbour relation: which points a given point selects and which points select it. Incoming selections are treated as weighted votes that help decide which local connections remain visible during a recursive forward-and-reverse traversal. Repeating the procedure for increasing $K$ reveals how groups persist or merge as the neighbourhood scale grows; for the reference inverse-square model before structural refinement, clusters can merge but do not split. Because graph connectivity can occasionally join distinct groups through a sparse bridge or a small region of overlap, we add an optional label-free refinement. It first tests whether an already formed component is better described by two or three Gaussian subpopulations, and accepts a subdivision only when the proposed groups are large enough and consistent with the visible KNN graph. Across eight synthetic datasets and $K=2,\ldots,16$, independent C and Python implementations produced identical partitions in all 120 reference runs. Refinement increased adjusted Rand index from $0.7817$ to $0.9627$ on a variable-density benchmark and from $0.8083$ to $0.9853$ on a sparse-bridge benchmark. Comparisons with seven external clustering methods show competitive performance while preserving a label-free cluster-construction process.
CommentsSubmitted to Knowledge and Information Systems (KAIS). 32 pages, 6 figures
