Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
530976 | Pattern Recognition | 2010 | 10 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Ibai Gurrutxaga, Iñaki Albisua, Olatz Arbelaitz, José I. Martín, Javier Muguerza, Jesús M. Pérez, Iñigo Perona,