Advances in Multivariate Data Analysis: Proceedings of the by Carmela Cappelli, Francesco Mola (auth.), Prof. Dr.

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By Carmela Cappelli, Francesco Mola (auth.), Prof. Dr. Hans-Hermann Bock, Prof. Marcello Chiodi, Prof. Antonino Mineo (eds.)

This quantity features a choice of papers awarded in the course of the biennial assembly of the category and knowledge research workforce (CLADAG) of the Societa Italiana di Statistica which used to be orga­ nized via the Istituto di Statistica of the Universita degli Studi di Palermo and held within the Palazzo Steri in Palermo on July 5-6, 2001. For this convention, and after checking the submitted four­ web page abstracts, fifty four papers have been admitted for presentation. They coated a wide range of subject matters from multivariate information research, with precise emphasis on class and clustering, computa­ tional information, time sequence research, and purposes in quite a few classical or contemporary domain names. A two-fold cautious reviewing technique ended in the choice of twenty-two papers that are offered during this vol­ ume. they impart both a brand new notion or technique, current a brand new set of rules, or crisis an engaging software. we now have clustered those papers into 5 teams as follows: 1. class tools with functions 2. Time sequence research and comparable equipment three. machine extensive ideas and Algorithms four. type and information research in Economics five. Multivariate research in technologies. In every one part the papers are prepared in alphabetical order. The editors - of them the organizers of the CLADAG confer­ ence - want to show their gratitude to the authors whose enthusiastic participation made the assembly attainable and extremely successful.

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Extra info for Advances in Multivariate Data Analysis: Proceedings of the Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, University of Palermo, July 5–6, 2001

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Analysis of Symboli c Data, Springer , Heid elberg , 1- 23. S. (1997) A model for ext reme ly het erogeneous clut ter . IEEE Trans actions on Geoscien ce and Rem ote Sensing, 1, 648-659. , DIDAY, E . (1996) A fuzzy symbolic pattern classifier. In : Diday, E . ) : Ordinal and Symbolic Data Analysis, Springer , Heidelberg, 92-102 . JAIN, A. K. (1988) Fundamentals of Digital Image Processing. Englewood Cliffs. Prentice Hall International Editions. LEE, J . Comput er Graphics and Image Processing, 17, 24-32.

1 Threenorm example The first example is a simulation study based on a two-class data set with 6-dimensional measurement vectors. , -a) . , a, -a) , where a = 2/(20)1 /2. Sixty training samples of 500 observations each were generated and 10000 observations have been considered as test set. These trees were fit using a procedure like CART (Breiman et al. (1984)) implemented in GAUSS , with the Gini criterion for splitting and using t he test set for pruning. Comparing pairs of the 60 trees obtained in this way, we have computed 1770 values for the measures (2), (4) and (10), considering both ways of weighting discrepancies for (4) and (10).

A) . , a, -a) , where a = 2/(20)1 /2. Sixty training samples of 500 observations each were generated and 10000 observations have been considered as test set. These trees were fit using a procedure like CART (Breiman et al. (1984)) implemented in GAUSS , with the Gini criterion for splitting and using t he test set for pruning. Comparing pairs of the 60 trees obtained in this way, we have computed 1770 values for the measures (2), (4) and (10), considering both ways of weighting discrepancies for (4) and (10).

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