Collection of algorithms in multiple programming languages.

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

Multi-level network clustering based on the Map Equation

Fast and Efficient Implementation of HDBSCAN in C++ using STL

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

Sequential and Parallel(using Open MP and Pthreads) Implementations(c++) of the K Means Clustering Algorithm and visualizing the results for a comparative study of the Speedup and Efficiency achieved in 3 different implementations

spatially-constrained clustering in R

gmm_diag and gmm_full: C++ classes for multi-threaded Gaussian mixture models and Expectation-Maximisation

C++ Implementation of the OPTICS algorithm compatible with the Point Cloud Library.

Implementation of the K-Means++ Algorithm for better centroid initializations than the standard version of K-Means Algorithm

Vienna Graph Clustering

Autonomous Dynamic Learning Apprentice System

Laser Line Segment Clustering Algorithm Based on Graph.

Discrete Optimization Algorithms

C++ implementation of a MCMC sampler for the (canonical) SBM

k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster.

RG (Randomized Greedy clustering), CGGC_RG (Core Groups Graph ensemble Clustering) or CGGCi_RG (Core Groups Graph ensemble Clustering Iterative) algorithms

Agglomerative hierarchical clustering for sparse similarity matrices.

Distributed Graph Clustering using the experimental Thrill framework

Implementation of EM using K-Means(Gaussian Mixture Model)