International Federation of Classification Societies The International Federation of Classification Societies (IFCS) is an agency for the dissemination of technical and scientific information concerning classification and data analysis in the broad sense and in as wide a· range of applications as possible; founded in 1985 in Cambridge (UK) from the following Scientific Societies and Groups: British Classification Society -BCS; Classification Society of North America -CSNA; Gesellschaft fUr Klassifikation -GfKl; Japanese Classification Society -JCS; Classification Group of Italian Statistical Society - COSIS; Societe Francophone de Classification -SFC. Now the IFCS includes the following Societies: Dutch-Belgian Classification Society - VOC; Polish Classification Section - SKAD; Portuguese Classification Association - CLAD; Group-at-Large; Korean Classification Society -KCS. Biannual Meeting of the Classification and Data Analysis Group of SIS The biannual meeting of the Classification and Data Analysis Group of Societa Italiana di Statistica (SIS) was held in Pescara, July 3 -4, 1997. The 69 papers presented were divided in 17 sessions. Each session was organized by a chairperson with two invited speakers and two contributed papers from a call for papers. All the works were referred. Furthermore, during the meeting a discussant was provided for each session. A short version of the papers (4 pages) was.published before the conference.

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Classification: Methodologies in Classification: A. Cerioli: Measuring the influence of individual observations and variables in cluster analysis.- P. DUrso, M. G. Pittau: Consensus classification for a set of multiple time series.- T. Di Battista, D. Di Spalatro: A bootstrap method for adaptive cluster sampling.- D. Iezzi, M. Vichi: Forecasting a classification.- Fuzzy Clustering and Fuzzy Methods: A. Bellacicco: Neural networks as a fuzzy semantic network of events.- L. Cerbara: Hierarchical fuzzy clustering: an example of spatio-temporal analysis.- G. Iacovacci: A new algorithm for semi-fuzzy clustering.- A. Maturo, B. Ferri: Fuzzy classification and hyperstructures : an application to evaluation of urban projects.- M.A. Milioli: Variable selection in fuzzy clustering.- Other Approaches for Classification: Discrimination and Classification: M. Alfo, P. Postiglione: Discriminant analysis using markovian automodels.- F. Esposito, D. Malerba, G. Semeraro, S. Caggese: Discretization of continuous-valued data in symbolic classification learning.- S. Ingrassia: Logistic discrimination by Kullback-Leibler type distance measures.- A. Montanari, D. Calo: An empirical discrimination algorithm based on projection pursuit density estimation.- Regression Tree and Neural Networks: R. Miglio, M. Pillati: Notes on methods for improving unstable classifiers.- F. Mola: Selection of cut points in generalized additive models.- R. Siciliano: Latent budget trees for multiple classification.- Multivariate and Multidimensional Data Analysis: Proximity Analysis and Multidimensional Scaling: G. Bove, R. Rocci: Methods for asymmetric three-way scaling.- S. Camiz: Comparison of Euclidean approximation of non-Euclidean distances.- A. Montanari, G. Soffritti: Analysing dissimilarities through multigraphs.- C. Quintano: Professional positioning based on dominant eigenvalue scores (DES), dimensional scaling (DS) and multidimensional scaling (MDS) synthesis of binary evaluations matrix of experts.- M. Vichi: Non-metric full-multidimensional scaling.- Factorial Methods: I. Corazziari: Dynamic factor analysis.- V. Esposito, G. Scepi: A non symmetrical generalised co-structure analysis for inspecting quality control data.- R. Lombardo, G. Tessitore: Principal surfaces constrained analysis.- R. Verde: Generalised canonical analysis on symbolic objects.- G. Vittadini: Analysis of qualitative variables in structural models with unique solutions.- Spatial Analysis: A. Capobianchi: Exploring multivariate spatial data: line transect data.- A. Giusti, A. Petrucci: On the assessment of geographical survey units using constrained classification.- L. Romagnoli: Kalman filter applied to non-causal models for spatial data.- Multiway Data Analysis: S. Bolasco, A. Morrone, F. Baiocchi: A paradigmatic path for statistical content analysis using an integrated package of textual data treatment.- M. Chiodi, A. M. Mineo: The analysis of auxological data by means of nonlinear multivariate growth curves.- M. Coli, L. Ipploliti, E. Nissi: The Kalman filter on three-way data matrix for missing data: a case study on sea water pollution.- P.A. Cornillon, P. Amenta, R. Sabatier: Three-way data arrays with double neighborhood relations as a tool to analyze a contiguity structure.- A. Lemmi, D. Stefano Gazzei: Firm performance analysis with panel data.- Multivariate Data Analysis: M.R. DEsposito, G. Ragozini: Detection of multivariate outliers by convex hulls.- M. Di Marzio, G. Lafratta: Reducing dimensionality effects on kernel density estimation: the bivariate gaussi