A Framework for Parallelizing Hierarchical Clustering Methods

Hierarchical clustering is a widely used tool in machine learning, several sequential hierarchical clustering algorithms are known and well-studied. These algorithms include top down divisive approaches such as bisecting -means, -median or -center, and bottom-up agglomerative approaches such as single-linkage, average-linkage, and centroid-linkage.