IIT-LAND / reval_clustering

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 (latest version v0.1.0):

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.

Manuscript

reval package functionalities are presented in our recent work that, as of now, can be found as a preprint. The experiments presented in the manuscript are in the Python file ./working_examples/manuscript_examples.py of the github folder. For reproducibility, all experiments were run with reval v0.1.0.

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

Landi, I., Mandelli, V., & Lombardo, M. V. (2021). reval: A Python package to determine best clustering solutions with stability-based relative clustering validation. Patterns, 2(4), 100228.

BibTeX alternative

@article{LANDI2021100228,
title = {reval: A Python package to determine best clustering solutions 
         with stability-based relative clustering validation},
journal = {Patterns},
volume = {2},
number = {4},
pages = {100228},
year = {2021},
issn = {2666-3899},
doi = {https://doi.org/10.1016/j.patter.2021.100228},
url = {https://www.sciencedirect.com/science/article/pii/S2666389921000428},
author = {Isotta Landi and Veronica Mandelli and Michael V. Lombardo},
keywords = {stability-based relative validation, 
            clustering, 
            unsupervised learning, 
            clustering replicability}
}