SEP/COP: An efficient method to find the best partition in hierarchical clustering based on a new cluster validity index

Authors:
Ibai Gurrutxaga;Iñaki Albisua;Olatz Arbelaitz;José I. Martín;Javier Muguerza;Jesús M. Pérez;Iñigo Perona
Affiliations:
Department of Computer Architecture and Technology, University of the Basque Country, Manuel Lardizabal 1, 20018 Donostia, Spain;Department of Computer Architecture and Technology, University of the Basque Country, Manuel Lardizabal 1, 20018 Donostia, Spain;Department of Computer Architecture and Technology, University of the Basque Country, Manuel Lardizabal 1, 20018 Donostia, Spain;Department of Computer Architecture and Technology, University of the Basque Country, Manuel Lardizabal 1, 20018 Donostia, Spain;Department of Computer Architecture and Technology, University of the Basque Country, Manuel Lardizabal 1, 20018 Donostia, Spain;Department of Computer Architecture and Technology, University of the Basque Country, Manuel Lardizabal 1, 20018 Donostia, Spain;Department of Computer Architecture and Technology, University of the Basque Country, Manuel Lardizabal 1, 20018 Donostia, Spain
Venue:
Pattern Recognition
Year:
2010

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Abstract

Hierarchical clustering algorithms provide a set of nested partitions called a cluster hierarchy. Since the hierarchy is usually too complex it is reduced to a single partition by using cluster validity indices. We show that the classical method is often not useful and we propose SEP, a new method that efficiently searches in an extended partition set. Furthermore, we propose a new cluster validity index, COP, since many of the commonly used indices cannot be used with SEP. Experiments performed with 80 synthetic and 7 real datasets confirm that SEP/COP is superior to the method currently used and furthermore, it is less sensitive to noise.