reval: A Python package to determine best clustering solutions with stability-based relative clustering validation.

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Landi, Isotta 
Mandelli, Veronica 
Lombardo, Michael V  ORCID logo

Determining the best partition for a dataset can be a challenging task because of the lack of a priori information within an unsupervised learning framework and the absence of a unique clustering validation approach to evaluate clustering solutions. Here we present reval: a Python package that leverages stability-based relative clustering validation methods to select best clustering solutions as the ones that replicate, via supervised learning, on unseen subsets of data. The implementation of relative validation methods can contribute to the theory of clustering by fostering new approaches for the investigation of clustering results in different situations and for different data distributions. This work aims at contributing to this effort by implementing a package that works with multiple clustering and classification algorithms, hence allowing both the automation of the labeling process and the assessment of the stability of different clustering mechanisms.

clustering, clustering replicability, stability-based relative validation, unsupervised learning
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Patterns (N Y)
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Elsevier BV