The book provides new developments in data analysis and statistical multivariate methods, computational statistics and algorithms, including new topics which are of central interest to modern statistics. The reader will find advanced methodologies and computational methods which are very helpful to analyze real phenomena characterized by large data bases. Furthermore, the volume includes papers devoted to original and innovative applications of recent statistical theory and complex approaches of statistical data analysis. TOC:Part I: Classification and Clustering.- Part II: Image Analysis and Signal Processing.- Part III: Data Visualization.- Part IV: Multivariate Analysis.- Part V: Web Based Teaching.- Part VI: Algorithms.- Part VII: Robustness.- Part on CD: Part VIII: Categorical Data Analysis.- Part IX: Multivariate Data Analysis II.- Part X: Classification and Clustering II.- Part XI: Data Mining.- Part XII: Biostatistics.- Part XIII: Resampling Methods.- Part XIV Functional Data Analysis.- Part XV: Time Series Analysis and Spatial Analysis.- Part XVI: Nonparametric Statistics and Smoothing.- Part XVII: Statistical Software and Optimization Algorithms.- Part XVIII: Computational Bayesian Methods.- Part XIX: Computational Methods in Offical Statistics.- Part XX: Computational Statistics in Finance, Industry and Economics.- Part XXI: Microarray Data Analysis.- Part XXII: Statistical Education and Web Based Teaching.- Part XXIII: Posters. International Association for Statistical Computing The International Association for Statistical Computing (IASC) is a Section of the International Statistical Institute. The objectives of the Association are to foster world-wide interest in e?ective statistical computing and to - change technical knowledge through international contacts and meetings - tween statisticians, computing professionals, organizations, institutions, g- ernments and the general public. The IASC organises its own Conferences, IASC World Conferences, and COMPSTAT in Europe. The 17th Conference of ERS-IASC, the biennial meeting of European - gional Section of the IASC was held in Rome August 28 - September 1, 2006. This conference took place in Rome exactly 20 years after the 7th COMP- STAT symposium which was held in Rome, in 1986. Previous COMPSTAT conferences were held in: Vienna (Austria, 1974); West-Berlin (Germany, 1976); Leiden (The Netherlands, 1978); Edimbourgh (UK, 1980); Toulouse (France, 1982); Prague (Czechoslovakia, 1984); Rome (Italy, 1986); Copenhagen (Denmark, 1988); Dubrovnik (Yugoslavia, 1990); Neuch¿ atel (Switzerland, 1992); Vienna (Austria,1994); Barcelona (Spain, 1996);Bristol(UK,1998);Utrecht(TheNetherlands,2000);Berlin(Germany, 2002); Prague (Czech Republic, 2004). Part I Classification and Clustering Issues of robustness and high dimensionality in cluster analysis Kaye Basford, Geoff McLachlan, Richard Bean 3(14) Fuzzy K-medoids clustering models for fuzzy multivariate time trajectories Renato Coppi, Pierpaolo D'Urso, Paolo Giordani 17(14) Bootstrap methods for measuring classification uncertainty in latent class analysis Jos . Dias, Jeroen K. Vermunt 31(12) A robust linear grouping algorithm Greet Pison, Stefan Van Aelst, Ruben H. Zamar 43(12) Computing and using the deviance with classification trees Gilbert Ritschard 55(12) Estimation procedures for the false discovery rate: a systematic comparison for microarray data Michael G. Schirnek, Tom Pavlik 67(14) A unifying model for biclustering Iven Van Mechelen, Jan Schepers 81(10) Part II Image Analysis and Signal Processing Non-rigid image registration using mutual information Frederik Maes, Emiliano D'Agostino, Dirk Loeckx, Jeroen Wouters, Dirk Vandermeulen, Paul Suetens 91(14) Musical audio analysis using sparse representations Mark D. Plumbley, Sarver A. Abdallah, Thomas Blumensath, Maria G. Jafari, Andrew Nesbit, Emmanuel Vincent, Beiming Wang 105(14) Robust correspondence recognition for computer vision Radim 119(14) Blind superresolution Filip roubek, Gabriel Crist bal, Jan Flusser 133(14) Analysis of Music Time Series Claus Weihs, Uwe Ligges, Katrin Sommer 147(16) Part III Data Visualization Tying up the loose ends in simple, multiple, joint correspondence analysis Michael Greenacre 163(24) 3 dimensional parallel coordinates plot and its use for variable selection Keisuke Honda, Junji Nakano 187(10) Geospatial distribution of alcohol-related violence in Northern Virginia Yasinin H. Said, Edward J. Wegman 197(12) Visualization in comparative music research Petri Toiviainen, Thomas Eerola 209(12) Exploratory modelling analysis: visualizing the value of variables Antony Unwin 221(10) Density estimation from streaming data using wavelets Edward J. Wegman, Kyle A. Caudle 231(14) Part IV Multivariate Analysis Reducing conservatism of exact small-sample methods of inference for discrete data Alan Agresti, Anna Gottard 245(16) Symbolic data analysis: what is it? Lynne Billard 261(10) A dimensional reduction method for ordinal three-way contingency table Luigi D'Ambra, Biagio Simonetti and Eric J. Beh 271(14) Operator related to a data matrix: a survey Yves Escoufier 285(14) Factor interval data analysis and its application Wang Huiwen, Henry M.K. Mok, Li Dapeng 299(14) Identifying excessively rounded or truncated data Kevin H. Knuth, J. Patrick Castle, Kevin R. Wheeler 313(12) Statistical inference and data mining: false discoveries control St ane Lallich, Olivier Teytaud and Elie Prudhomme 325(12) Is 'Which model...?' the right question? Nicholas T. Longford 337(14) Use of latent class regression models with a random intercept to remove the effects of the overall response rating level Jay Magidson, Jeroen K. Vermunt 351(10) Discrete functional data analysis