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.

Clustering Application made in Visual C++

Clustering methods implementations in C++: Lloyd, K-Means, K-Means++, PAM

Projeto de Análise de Redes Sociais cuja intenção é encontrar a clique máxima em um grafo.

C++ Implementation of poLCA (R package)

DAOR Parameter-free Embedding Framework for Large Graphs (Networks)

DAOC (Deterministic and Agglomerative Overlapping Clustering algorithm): Stable Clustering of Large Networks

Genie: A Fast and Robust Hierarchical Clustering Algorithm (this R package has now been superseded by genieclust)

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