title: Hausdorff clustering.
abstract: A clustering algorithm based on the Hausdorff distance is analyzed and compared to the single, complete, and average linkage algorithms. The four clustering procedures are applied to a toy example and to the time series of financial data. The dendrograms are scrutinized and their features compared. The Hausdorff linkage relies on firm mathematical grounds and turns out to be very effective when one has to discriminate among complex structures.
authors.affiliation: Trading Risk Management, Holding, UniCredit SpA, 20121 Milan, Italy.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Basalto
authors.forename: Roberto
authors.initials: R
authors.lastname: Bellotti
authors.forename: Francesco
authors.initials: F
authors.lastname: De Carlo
authors.forename: Paolo
authors.initials: P
authors.lastname: Facchi
authors.forename: Ester
authors.initials: E
authors.lastname: Pantaleo
authors.forename: Saverio
authors.initials: S
authors.lastname: Pascazio
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001703:Biophysics / Q000379:methods*; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D008954:Models, Biological; D015233:Models, Statistical; D008962:Models, Theoretical