By Carmela Cappelli, Francesco Mola (auth.), Prof. Dr. Hans-Hermann Bock, Prof. Marcello Chiodi, Prof. Antonino Mineo (eds.)
This quantity encompasses a number of papers awarded through the biennial assembly of the type and knowledge research staff (CLADAG) of the Societa Italiana di Statistica which used to be orga nized through 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 variety of themes from multivariate facts research, with specified emphasis on category and clustering, computa tional information, time sequence research, and functions in a number of classical or contemporary domain names. A two-fold cautious reviewing procedure ended in the choice of twenty-two papers that are offered during this vol ume. they communicate both a brand new inspiration or technique, current a brand new set of rules, or crisis an attractive program. we've clustered those papers into 5 teams as follows: 1. category tools with functions 2. Time sequence research and similar equipment three. laptop extensive concepts and Algorithms four. type and knowledge research in Economics five. Multivariate research in technologies. In each one part the papers are prepared in alphabetical order. The editors - of them the organizers of the CLADAG confer ence - want to exhibit their gratitude to the authors whose enthusiastic participation made the assembly attainable and intensely successful.
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Additional resources 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
Moreover , the decreas e in impurity achieved by splitting a node into two sub-nodes is not obtained as the difference between the total heterogeneity within the node and the het erogeneity within the two obtained sub-nodes. This is the reason why we suggest to construct classification trees measuring heterogeneity by referring to (8), which is a proper exte nsion of the mutual dispersion measure to the ordinal case, and definin g a criterion to select best splits on the basis of the difference between total and within het erogeneity.
FREUND , Y. and SCHAPIRE, R . (1996) : Experiments with a New Boosting Algorithm. Machin e Learning: Proceedings of the Thirteenth International Conf erence, July 1996. Proximity Measures Between Classification Trees 37 FREUND, Y. and SCHAPIRE, R. (1999): A Short Introduction to Boosting. Journal of Japanese Society for Artificial Intelligence, 14, 771-780. GORDON , A. D . (1999) : Classification, 2nd edition. Chapman and Hall/CRC, London. HUBERT, L. and ARABIE, P. (1985): Comparing Partitions.
Ap ) (Space Time Autoregressive Generalised) model (Di Giacinto 1994; Terzi 1995) P Y( ', t) = Ah LL
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 by Carmela Cappelli, Francesco Mola (auth.), Prof. Dr. Hans-Hermann Bock, Prof. Marcello Chiodi, Prof. Antonino Mineo (eds.)