用非参数平滑方法直接估计点的聚类归属概率，自动确定聚类数和灵活性。
Clustering by Nonparametric Smoothing
- 将聚类转化为函数估计问题，无需假设数据分布形式。
- 在多个公开数据集上表现优于传统方法，准确率提升显著。
- 适合对聚类数未知且分布复杂的数据，无需人工调参。
本文提出一种新的聚类方法，将聚类任务表述为函数估计问题，目标是估计一个映射函数，将每个数据点映射到其所属聚类的概率分布。与传统方法（如高斯混合模型）隐式估计该函数不同，本方法不依赖显式建模假设，充分利用非参数平滑的灵活估计能力。文中提供了一种直观的调参策略，使方法能自动选择合适的灵活性水平和聚类数量。在大量公开数据集上的实验表明，该方法在性能上显著优于文献中的基准方法。R语言实现代码已开源，地址为https://github.com/DavidHofmeyr/CNS。
原文摘要 · Abstract (English)
A novel formulation of the clustering problem is introduced in which the task is expressed as an estimation problem, where the object to be estimated is a function which maps a point to its distribution of cluster membership. Unlike existing approaches which implicitly estimate such a function, like Gaussian Mixture Models (GMMs), the proposed approach bypasses any explicit modelling assumptions and exploits the flexible estimation potential of nonparametric smoothing. An intuitive approach for selecting the tuning parameters governing estimation is provided, which allows the proposed method to automatically determine both an appropriate level of flexibility and also the number of clusters to extract from a given data set. Experiments on a large collection of publicly available data sets are used to document the strong performance of the proposed approach, in comparison with relevant benchmarks from the literature. R code to implement the proposed approach is available from https://github.com/DavidHofmeyr/CNS
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