hdbscan — Peer-reviewed • Rank 33.3 • Science 95%

hdbscan: Hierarchical density based clustering - Published in JOSS (2017)

Persistable — Peer-reviewed • Rank 9.5 • Science 93%

Persistable: persistent and stable clustering - Published in JOSS (2023)

Local clustering — Peer-reviewed • Rank 8.3 • Science 93%

Local clustering - Published in JOSS (2018)

gmm_diag and gmm_full — Peer-reviewed • Rank 2.4 • Science 93%

gmm_diag and gmm_full: C++ classes for multi-threaded Gaussian mixture models and Expectation-Maximisation - Published in JOSS (2017)

networkanalysis-ts • Rank 10.5 • Science 77%

TypeScript port of the Java networkanalysis package that provides data structures and algorithms for network analysis.

publicationclassification • Rank 2.8 • Science 77%

Java package for creating a multi-level classification of scientific publications based on citation links between publications.

infomap • Rank 16.5 • Science 44%

Multi-level network clustering based on the Map Equation

genieclust • Rank 16.1 • Science 39%

Genie: Fast and Robust Hierarchical Clustering with Noise Point Detection - in Python and R

unsupervised_analysis • Rank 4.7 • Science 49%

A general purpose Snakemake workflow and MrBiomics module to perform unsupervised analyses (dimensionality reduction & cluster analysis) and visualizations of high-dimensional data.

kmodes • Rank 22.2 • Science 23%

Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data

spatialcluster • Rank 3.7 • Science 39%

spatially-constrained clustering in R

itclust • Rank 8.6 • Science 33%

Iterative transfer learning with neural network improves clustering and cell type classification in single-cell RNA-seq analysis

s4rdm3x — Peer-reviewed • Rank 3.7 • Science 36%

s4rdm3x: A Tool Suite to Explore Code to Architecture Mapping Techniques - Published in JOSS (2021)

icellr • Rank 16.2 • Science 23%

Single (i) Cell R package (iCellR) is an interactive R package to work with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST)).

scedar • Rank 4.8 • Science 23%

Single-cell exploratory data analysis for RNA-Seq

https://github.com/aryashah2k/datalogy-customer-segmentation-data-science-internship • Rank 2.9 • Science 10%

A Repository Maintaining My Summer Internship Work At Datalogy As A Data Science Intern Working On Customer Segmentation Models Using Heirarchical Clustering, K-Means Clustering And Identifying Loyal Customers Based On Creation Of Recency, Frequence, Monetary (RFM) Matrix.

https://github.com/chris-santiago/kmeans • Rank 0.7 • Science 10%

A simple implementation of K-Means & K-Medoids Clustering

comstream • Science 67%

In this project, we implemented a topic detection system on Twitter. This system reads tweets from a data stream and assigns them to one of the existing clusters or a new one. Each cluster acts as an agent, which makes the proposed approach a multi-agent system. There is also a coordinator, who monitors the whole system and coordinates the agent.

cluster_htrc • Science 67%

Identifying the boundaries of main content of fiction and non-fiction works in the HathiTrust Extracted Features dataset.

corrp — Peer-reviewed • Science 93%

corrp: An R package for multiple correlation-like analysis and clustering in mixed data - Published in JOSS (2025)

https://github.com/cwtsleiden/networkanalysis • Science 23%

Java package that provides data structures and algorithms for network analysis.

https://github.com/acil-group/dvfa • Science 10%

Dual Vigilance Fuzzy ART - Companion MATLAB Code

https://github.com/crmawande/clustering_algorithms • Science 13%

silhouette • Science 39%

Silhouette-Based Diagnostics for Standard, Soft, and Multi-Way Clustering

https://github.com/acil-group/ddvfa • Science 10%

Distributed Dual Vigilance Fuzzy ART - Companion MATLAB Code

Banksy • Science 49%

BANKSY: spatial clustering

torchsom • Science 49%

TorchSOM is a PyTorch-based library for training Self-Organizing Maps (SOMs), a type of unsupervised learning algorithm used for clustering and dimensionality reduction. Designed for scalability and ease of use, this implementation is optimized for large-scale data.