A high performance implementation of HDBSCAN clustering.

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

Clustered Forward/Deferred renderer with Physically Based Shading, Image Based Lighting and a whole lot of OpenGL.

Automatically exported from code.google.com/p/cdhit

Implementation of the Leiden algorithm for various quality functions to be used with igraph in Python.

Collection of algorithms in multiple programming languages.

Multi-level network clustering based on the Map Equation

Flutter GraphView is used to display data in graph structures. It can display Tree layout, Directed and Layered graph. Useful for Family Tree, Hierarchy View.

Clustering routines for the unit sphere

NMFLibrary: Non-negative Matrix Factorization (NMF) Library: Version 2.1

Clustering / Subspace Clustering Algorithms on MATLAB

A simple python implementation of Fuzzy C-means algorithm.

A Python implementation of COP-KMEANS algorithm

A suite of classification clustering algorithm implementations for Java. A number of partitional, hierarchical and density-based algorithms including DBSCAN, k-Means, k-Medoids, MeanShift, Affinity Propagation, HDBSCAN and more.

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

Various scripts to support deeplabcut and what to do afterwards!

Implements "Clustering a Million Faces by Identity"

C4E, a JVM friendly library written in Scala for both local and distributed (Spark) Clustering.

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)).

Clustering methods in Machine Learning includes both theory and python code of each algorithm. Algorithms include K Mean, K Mode, Hierarchical, DB Scan and Gaussian Mixture Model GMM. Interview questions on clustering are also added in the end.