reval

reval: stability-based relative clustering validation method to determine the best number of clusters

Determining the number of clusters that best partitions a dataset can be a challenging task because of 1) the lack of a priori information within an unsupervised learning framework; and 2) 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 determine best clustering solutions, as described in [1].

Statistical software, both in R and Python, usually compute internal validation metrics that can be leveraged to select the number of clusters that best fit the data and open-source software solutions that easily implement relative clustering techniques are lacking. The advantage of a relative approach over internal validation methods lies in the fact that internal metrics exploit characteristics of the data itself to produce a result, whereas relative validation converts an unsupervised clustering algorithm into a supervised classification problem, hence enabling generalizability and replicability of the results.

Requirements

python>=3.6

Installing

From github:

git clone https://github.com/IIT-LAND/reval_clustering
pip install -r requirements.txt

PyPI alternative:

pip install reval

Documentation

Code documentation can be found here. Documents include Python code descriptions, reval usage examples, performance on benchmark datasets, and common issues that can be encountered related to a dataset number of features and samples.

Refrences

[1] Lange, T., Roth, V., Braun, M. L., & Buhmann, J. M. (2004). Stability-based validation of clustering solutions. Neural computation, 16(6), 1299-1323.

Cite as

Isotta Landi, Veronica Mandelli, & Michael Vincent Lombardo. (2020, June 29). 
reval: stability-based relative clustering validation method to determine the best number of clusters 
(Version v1.0.0). Zenodo. http://doi.org/10.5281/zenodo.3922334

BibTeX alternative

@software{isotta_landi_2020_3922334,
          author       = {Isotta Landi and
                          Veronica Mandelli and
                          Michael Vincent Lombardo},
          title        = {{reval: stability-based relative clustering 
                           validation method to determine the best number of
                           clusters}},
          month        = jun,
          year         = 2020,
          publisher    = {Zenodo},
          version      = {v1.0.0},
          doi          = {10.5281/zenodo.3922334},
          url          = {https://doi.org/10.5281/zenodo.3922334}
        }