International Federation of Classification Societies The International Federation of Classification Societies (lFCS) is an agency for the dissemination of technical and scientific information concerning classification and multivariate data analysis in the broad sense and in as wide a range of applications as possible; founded in 1985 in Cambridge (UK) by the following Scientific Societies and Groups: - British Classification Society - BCS - Classification Society of North America - CSNA - Gesellschaft fUr Klassification - GfKI - Japanese Classification Society - JCS - Classification Group ofItalian Statistical Society - CGSIS - Societe Francophone de Classification - SFC Now the IFCS includes also the following Societies: - Dutch-Belgian Classification Society - VOC - Polish Classification Section - SKAD - Portuguese Classification Association - CLAD - Group at Large - Korean Classification Society - KCS IFCS-98, the Sixth Conference of the International Federation of Classification Societies, was held in Rome, from July 21 to 24, 1998. Five preceding conferences were held in Aachen (Germany), Charlottesville (USA), Edinburgh (UK), Paris (France), Kobe (Japan).

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Methodologies in Classification: Clustering and Classification: J.D. Carroll, A. Chaturvedi: K-Midranges Clustering.- A. Cerioli: A New Method for Detecting Influential Observations in Nonhierarchical Cluster Analysis.- J.C. Gower, G.J.S.Ross: Non-Probabilistic Classification.- A. Hardy, P. Andre: An Investigation of Nine Procedures for Detecting the Structure in a Data Set.- C. Hennig: Clustering and Outlier Identification: Fixed Point Cluster Analysis.- S. Korenjak-Cerne, V. Batagelj: Clustering Large Datasets of Mixed Units.- J.A. Martin-Fernandez, C. Barcelo-Vidal, V. Pawlowsky-Glahn: A Critical Approach to Non-Parametric Classification of Compositional Data.- F. Murtagh, J.L. Stark, M. Berry: Clustering Based on Wavelet Transform: Applications to Point Pattern Clustering and to High-Dimensional Data Analysis.- Comparison and Consensus of Classifications: C. Hayashi, K. Yamaoka: Beyond Simpon`s Paradox: One Problem in Data Science.- F.J. Lapointe: For Consensus (With Branch Lengths).- B. Leclerc: Consensus of Classifications: The Case of Trees.- J. L. Thorley, M. Wilkinson, M. Charleston: The Information Content of Consensus Trees.- Fuzzy Clustering and Fuzzy Methods: S. Bodjanova: Compatible and Complementary Fuzzy Partitions.- S. Miyamoto, K. Umayahara: Two Methods of Fuzzy c-Means and Classification Functions.- M. Ryoke, Y. Nakamori, H. Tamura: Dynamic Determination of Mixing Parameters in Fuzzy Clustering.- M. Sato-Ilic, Y. Sato: A Dynamic Additive Fuzzy Clustering Model.- S. Turpin-Dhilly, C. Botte-Lecocq: Application of Fuzzy Mathematical Morphology for Pattern Classification.- Optimization in Classification and Constrained Classification: K.Bachar, I.-C. Lerman: Statistical Conditions for a Linear Complexity for an Algorithm of Hierarchical Classification Under Constraint of Contiguity.- V. Batagelj, A. Ferligoj: Constrained Clustering Problems.- T. Gastaldi, D. Vicari: A Constrained Clusterwise Procedure for Segmentation.- L. Gueguen, R. Vignes, J. Lebbe: Maximal Predictive Clustering with Order Constraint: a Linear and Optimal Algorithm.- I. Lari, M. Maravalle, B. Simeone: A Linear Programming Based Heuristic for a Hard Clustering Problem on Trees.- M. Mizuta: Two Principal Points of Symmetric Distributions.- N, Nicoloyannis, M. Terrenoire, D. Tounissoux: Pertinence for a Classification.- J. Trejos, A. Murillo, E. Piza: Global Stochastic Optimization Techniques Applied to Partitioning.- Probabilistic Modelling Methods in Classification and Pattern Recognition: H. Bensmail, J.J. Meulman: MCMC Inference for Model-Based Cluster Analysis.- L. Györfi, M. Horvath: On the Asymptotic Normality of a Resubstitution Error Estimate.- G. V. Orman: Stochastic Methods for Generative Systems Analysis.- H.J. Vos: Compensatory Rules for Optimal Classification with Mastery Scores.- Other Approaches for Classification: Discrimination, Neural Network, Regression Tree: Discrimination and Classification: V. Bertholet, J.P. Rasson, S. Lissoir: About the Automatic Detection of Training Sets for Multispectral Images Classification.- A.P.Duarte Silva: A 'Leaps and Bounds' Algorithm for Variable Selection in Two Group Discriminant Analysis.- M.-H. Huh, K-S. Yang: Canonical Discriminant Analysis of Multinomial Samples with Applications to Textual Data.- I.C. Lerman, J.F. Pinto da Costa: How to Extract Predictive Binary Attributes from a Categorical One.- L. Lizzani: A Density Distance Based Approach to Projection Pursuit Discriminant Analysis.- A. Montanari, D. G. Calo: Two Group Linear Discrimination Based on