Lumbermark通过切割最小生成树枝干，自动发现不同大小密度的聚类。
Lumbermark: Resistant Clustering by Chopping Up Mutual Reachability Minimum Spanning Trees
- 用互达距离构建最小生成树，逐步砍断长分支以分离聚类
- 对噪声和离群点不敏感，能处理任意形状与密度差异的聚类
- 可指定聚类数量，适合需要可控聚类结果的数据科学家
我们提出Lumbermark，一种鲁棒的分裂式聚类算法，能够检测不同大小、密度和形状的聚类。该算法通过迭代切除互达距离最小生成树中由突出段连接的大枝干实现聚类分割。互达距离平滑数据分布，降低低密度点（如簇间噪声或边缘离群点）的影响。Lumbermark可视为HDBSCAN的替代方案，能生成用户指定大小的聚类划分。该方法已开源，提供Python和R版本的快速易用实现。我们在基准数据集上验证了其性能，期望该方法能为各领域的数据科学家和实践者提供帮助。
原文摘要 · Abstract (English)
We introduce Lumbermark, a robust divisive clustering algorithm capable of detecting clusters of varying sizes, densities, and shapes. Lumbermark iteratively chops off large limbs connected by protruding segments of a dataset's mutual reachability minimum spanning tree. The use of mutual reachability distances smoothens the data distribution and decreases the influence of low-density objects, such as noise points between clusters or outliers at their peripheries. The algorithm can be viewed as an alternative to HDBSCAN that produces partitions with user-specified sizes. A fast, easy-to-use implementation of the new method is available in the open-source 'lumbermark' package for Python and R. We show that Lumbermark performs well on benchmark data and hope it will prove useful to data scientists and practitioners across different fields.
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