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Clustering Evaluation Python Packages

Python packages with the GitHub topic clustering-evaluation. Sorted by relevance, with stars and monthly downloads.
scikit-learn-contrib
hdbscan

A high performance implementation of HDBSCAN clustering.

3.1M 3K 533
thieu1995
permetrics

Artificial intelligence (AI, ML, DL) performance metrics implemented in Python

23K 91 22
hhromic
bcubed

Simple Extended BCubed implementation in Python for clustering evaluation

4K 51 9
scikit-learn-contrib
bf44-hdbscan-0844

A high performance implementation of HDBSCAN clustering.

3K 3K 533
gagolews
quitefastmst

quitefastmst: Euclidean and Mutual Reachability Minimum Spanning Trees

3K 1 0
scikit-learn-contrib
hdbscan-314

A high performance implementation of HDBSCAN clustering.

2K 3K 533
scikit-learn-contrib
psgg-hdbscan-0844

A high performance implementation of HDBSCAN clustering.

1K 3K 533
MohammadRSalmanpour
allmetrics

A Unified Python Library for Standardized Metric Evaluation in Machine Learning

1K 3 0
alashkov83
s-dbw

S_Dbw validity index. Adapted for DBSCAN (and similar)

915 12 5
eren-ck
finchpy

A Python implementation of "FINCH Clustering Algorithm (CVPR 2019)"

615 9 2
gagolews
clustering-benchmarks

A Framework for Benchmarking Clustering Algorithms

528 44 8
semoglou
extclustval

A lightweight Python package for external clustering validation metrics.

321 0 0
semoglou
composite-silhouette

A clustering evaluation framework that combines micro- and macro-averaged silhouette scores into a composite metric using statistical weighting.

269 1 1
xGabrielR
cluster-ss

Improving Clustering Problem Analysis with a simple Silhouette Metric Support and Sklearn Estimators Fit

248 0 0
hj-n
btwim

The implementation of between dataset internal measures

244 1 0
semoglou
intclustval

A lightweight Python package for internal clustering validation metrics.

225 0 0
hugo-strang
silhouette-upper-bound

An upper bound of the Average Silhouette Width

187 7 1
nezumiCodes
bcubed-metrics

A package to calculate BCUBED precision, recall, and F1-score for clustering evaluation.

144 4 0
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