Download Advanced Statistical Methods for the Analysis of Large by Agostino Di Ciaccio, Mauro Coli, José Miguel Angulo Ibáñez PDF

By Agostino Di Ciaccio, Mauro Coli, José Miguel Angulo Ibáñez

ISBN-10: 3642210368

ISBN-13: 9783642210365

The subject matter of the assembly used to be “Statistical tools for the research of huge Data-Sets”. lately there was expanding curiosity during this topic; in truth a massive volume of knowledge is usually to be had yet typical statistical suggestions usually are not like minded to coping with this type of info. The convention serves as an enormous assembly element for eu researchers engaged on this subject and a couple of eu statistical societies participated within the association of the development.   The e-book comprises forty five papers from a range of the 156 papers accredited for presentation and mentioned on the convention on “Advanced Statistical equipment for the research of huge Data-sets.”

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Extra resources for Advanced Statistical Methods for the Analysis of Large Data-Sets (Studies in Theoretical and Applied Statistics / Selected Papers of the Statistical Societies)

Sample text

The location of the prototypes are Salerno and Ancona. The spatio-functional models that describe the two area are: fci . s ; Âs / D C hAci I i C hBci I Âi C hCci I Âi i D 1; 2 (13) where Aci , Bci are respectively the coefficient functions of the atmospheric pressure and of air temperature and Cci is the interaction function among atmospheric pressure and air temperature. Thus we can observe for the first cluster that the function Ac1 has a crescent shape with variability in the range Œ1003C I 1006C  and of the function Bc1 has a decrescent shape with variability in the range Œ 0:1hP aI 8:8hP a; while for the second cluster we have more variability in the range Œ1004C I 1010C  for the function Ac2 and for the function Bc2 in the range Œ4:7hP aI 12:2hP a.

Joint Clustering and Alignment of Functional Data 35 Fig. 1 Schematic flowcharts of the Procrustes continuous registration algorithm (left) and the functional k-mean clustering algorithm (right). Index i refers to the sample unit while index k to the cluster The aim of the k-mean clustering algorithm is instead to cluster functional data by decoupling within and between-cluster variability (in this context within and between-cluster amplitude variability); this task is here achieved by iteratively performing an identification step and an assignment step.

On the evaluated dataset this involves to set C D 3 for the first two analyses and C D 2 for the third one. Moreover to initialize the clustering procedures, we run a standard k-means algorithm on the spatial locations of the observed data, such to get a partitioning of data into spatially contiguous regions. By the results of the first two analysis, the 3 obtained clusters include quite similar stations and areas but are characterized by different prototypes. Especially, looking at the clustering structure of pressure curves, the clusters contain respectively 10; 11; 5 elements.

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