A General Framework for Agglomerative Hierarchical Clustering Algorithms

This paper presents a general framework for agglomerative hierarchical clustering based on graphs. Specifying an inter-cluster similarity measure, a subgraph of the similarity graph, and a cover routine, different hierarchical agglomerative clustering algorithms can be obtained. 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 comparable clustering quality.

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Reynaldo Gil-García, José Manuel Badía-Contelles, Aurora Pons-Porrata
• Keywords: Agglomerative Hierarchical Clustering, Computer Vision, Hierarchical Agglomerative Clustering, ICPR 2006, Traditional Hierarchical Algorithms