DeBaCl: A Python Package for Interactive DEnsity-BAsed CLustering

摘要

The level set tree approach of Hartigan (1975) provides a probabilisticallybased and highly interpretable encoding of the clustering behavior of adataset. By representing the hierarchy of data modes as a dendrogram of thelevel sets of a density estimator, this approach offers many advantages forexploratory analysis and clustering, especially for complex andhigh-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 theoreticalperspective, reliance on asymptotic approximations. To make it easier forpractitioners to capture the advantages of level set trees, we have written thePython package DeBaCl for DEnsity-BAsed CLustering. In this article weillustrate how DeBaCl's level set tree estimates can be used for difficultclustering tasks and interactive graphical data analysis. The package isintended to promote the practical use of level set trees through improvementsin computational efficiency and a high degree of user customization. Inaddition, the flexible algorithms implemented in DeBaCl enjoy finite sampleaccuracy, as demonstrated in recent literature on density clustering. Finally,we show the level set tree framework can be easily extended to deal withfunctional data.