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

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

The subject of the assembly was once “Statistical tools for the research of enormous Data-Sets”. lately there was expanding curiosity during this topic; in truth a massive volume of knowledge is usually on hand yet usual statistical strategies should not well matched to dealing with this sort of info. The convention serves as a massive assembly element for ecu researchers engaged on this subject and a few ecu statistical societies participated within the association of the development.   The booklet contains 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)

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The value of CA applying to the SVC algorithm is 1:8834 104 ; using SVC we obtain an higher value of the CATANOVA index, we can conclude that, with this dataset, SVC performs better than k-means. 4 Conclusion This method can be an alternative to traditional clustering methods when dealing with large data-sets of categorical data. The first issue solved is the quantification of categorical data with the MCA that reduces the dimensionality without losing nonlinear relations between variables. The second is the adaptation of the cone cluster labeling method, used in the case of Gaussian kernel, to the case of polynomial kernel.

ICTAI. IEEE Computer Society, Washington, DC, 529-535, 2007. H. Cardot, F. Ferraty, P. Sarda. Functional linear model. Statistics and Probability Letters, 45:11– 22, 1999. E. Diday. La Methode K des nuees K dynamiques. Rev. Appl. XXX, 2, 19–34, 1971. P. Delicado, R. Giraldo, J. Mateu. Geostatistics for functional data: An ordinary kriging approach. net/2117/1099, Universitat Politecnica de Catalunya, 2007. P. Delicado, R. Giraldo, C. Comas, J. Mateu. Statistics for Spatial Functional data. Environmetrics.

References C. Abraham, P. Corillon, E. Matnzer-Lober, R N. Molinari. Unsupervised curve clustering using B-splines. Scandinavian Journal of Statistics, 30, 581–595, 2005. , Petit. Spatio-temporal Functional Regression on Paleoecological Data, Functional and Operatorial Statistics, 54-56. Physica-Verlag HD, 2008. K. Blekas, C. Nikou, N. Galatsanos, N. V. Tsekos. Curve Clustering with Spatial Constraints for Analysis of Spatiotemporal Data. In Proceedings of the 19th IEEE international Conference on Tools with Artificial intelligence - Volume 01 (October 29 - 31, 2007).

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