DeBaCl：一个基于交互式密度聚类的Python程序包
DeBaCl: A Python Package for Interactive DEnsity-BAsed CLustering
摘要：
Hartigan（1975）的水平集树方法提供了一种基于概率的、高度可解释的数据集聚类行为编码。通过将数据模式的层次结构表示为密度估计器的水平集的树状图，这种方法为探索性分析和聚类提供了许多优点，特别是对于复杂和高维数据。存在几个R包的水平集树估计，但其实际用途是有限的计算效率低，缺乏交互式图形功能，从理论的角度来看，依赖于渐近近似。为了让实践者更容易地捕捉水平集树的优点，我们编写了Python包DeBaCl用于基于密度的聚类。在这篇文章中，我们将说明如何DeBaCl的水平集树估计可以用于困难的聚类任务和交互式图形数据分析。该软件包旨在通过提高计算效率和高度的用户定制来促进水平集树的实际使用。此外，DeBaCl中实现的灵活算法具有有限的样本精度，正如最近的密度聚类文献所证明的那样。最后，我们展示了水平集树框架可以很容易地扩展到处理功能数据。
英文摘要：
The level set tree approach of Hartigan (1975) provides a probabilistically based and highly interpretable encoding of the clustering behavior of a dataset. By representing the hierarchy of data modes as a dendrogram of the level sets of a density estimator, this approach offers many advantages for exploratory analysis and clustering, especially for complex and high-dimensional data. Several R packages exist for level set tree estimation, but their practical usefulness is limited by computational inefficiency, absence of interactive graphical capabilities and, from a theoretical perspective, reliance on asymptotic approximations. To make it easier for practitioners to capture the advantages of level set trees, we have written the Python package DeBaCl for DEnsity-BAsed CLustering. In this article we illustrate how DeBaCl's level set tree estimates can be used for difficult clustering tasks and interactive graphical data analysis. The package is intended to promote the practical use of level set trees through improvements in computational efficiency and a high degree of user customization. In addition, the flexible algorithms implemented in DeBaCl enjoy finite sample accuracy, as demonstrated in recent literature on density clustering. Finally, we show the level set tree framework can be easily extended to deal with functional data.
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。
