Condicions d'accésAccés restringit per política de l'editorial
This paper, arising from population studies, develops clustering algorithms for identifying patterns in data. Based on the concept of geometric variability, we have developed one polythetic-divisive and three agglomerative algorithms.
The effectiveness of these procedures is shown by relating them to classical clustering algorithms. They are very general since they do not impose constraints on the
type of data, so they are applicable to general (economics, ecological, genetics…)
studies. Our major contributions include a rigorous formulation for novel clustering
algorithms, and the discovery of new relationship between geometric variability and clustering. Finally, these novel procedures give a theoretical frame with an intuitive interpretation to some classical clustering methods to be applied with any type of data, including mixed data. These approaches are illustrated with real data on Drosophila chromosomal inversions.
CitacióIrigoien, I. [et al.]. GEVA: geometric variability-based approaches for identifying patterns in data. "Computational statistics", 15 Octubre 2009, vol. 25, núm. 2, p. 241-255.