Characterization of Linkage-based Clustering

Clustering is a central unsupervised learning task with a wide variety of applications. Not surprisingly, there exist many clustering algorithms. However, unlike classification tasks, in clustering, different algorithms may yield dramatically different outputs for the same input sets. A major challenge is to develop tools that may help select the more suitable algorithm for a given clustering task. We propose to address this problem by distilling abstract properties of clustering functions that distinguish between the types of input-output behaviors of different clustering paradigms. In this paper we make a significant step in this direction by providing such property based characterization for the class of linkage based clustering algorithms. Linkage-based clustering is one the most commonly used and widely studied clustering paradigms. It includes popular algorithms like Single Linkage and enjoys simple efficient algorithms. On top of their potential merits for helping users decide ...

• Type: Journal
• Year: 2010
• Where: COLT
• Authors: Margareta Ackerman, Shai Ben-David, David Loker
• Keywords: Abstract Properties, Algorithms, Clustering Paradigms, COLT 2010, Machine Learning