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Determining the best partition for a dataset can be a challenging taskbecause of 1) the lack of a priori information within an unsupervised learningframework; and 2) the absence of a unique clustering validation approach toevaluate clustering solutions. Here we present reval: a Python package thatleverages stability-based relative clustering validation methods to determinebest clustering solutions as the ones that best generalize to unseen data.Statistical software, both in R and Python, usually rely on internal validationmetrics, such as silhouette, to select the number of clusters that best fitsthe data. Meanwhile, open-source software solutions that easily implementrelative clustering techniques are lacking. Internal validation methods exploitcharacteristics of the data itself to produce a result, whereas relativeapproaches attempt to leverage the unknown underlying distribution of datapoints looking for generalizable and replicable results. The implementation ofrelative validation methods can further the theory of clustering by enrichingthe already available methods that can be used to investigate clusteringresults in different situations and for different data distributions. This workaims at contributing to this effort by developing a stability-based method thatselects the best clustering solution as the one that replicates, via supervisedlearning, on unseen subsets of data. The package works with multiple clusteringand classification algorithms, hence allowing both the automatization of thelabeling process and the assessment of the stability of different clusteringmechanisms.

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