This paper presents a general framework for agglomerative hierarchical clustering based on graphs. Different hierarchical agglomerative clustering algorithms can be obtained from this framework, by specifying an inter-cluster similarity measure, a subgraph of the â-similarity graph, and a cover routine. We also describe two methods obtained from this framework called Hierarchical Compact Algorithm and Hierarchical Star Algorithm. These algorithms have been evaluated using standard document collections. The experimental results show that our methods are faster and obtain smaller hierarchies than traditional hierarchical algorithms while achieving a similar clustering quality.