Data Analysis, Classification, and Related Methods

<B>Cluster Analysis: Cluster Analysis and Mixture</B> <B>Models:</B> <I>J. </I> <I>Hartigan:</I> Classifier Probabilities.- <I>R. Hoberg:</I> Cluster Analysis Based on Data Depth.- <I>Y. Sato:</I> An Autonomous Clustering Technique.- <I>M. Jardino:</I> Unsupervised Non-hierarchical Entropy-based Clustering.- <I>V. Makarenkov, </I> <I>P. Legendre:</I> Improving the Additive Tree Representation of a Dissimilarity Matrix Using Reticulations.- <I>A. Cjok:</I> Double Versus Optimal Grade Clusterings.- <I>I. Hajnal, G. Loosveldt:</I> The Effects of Initial Values and the Covariance Structure of the Recovery of Some Clustering Methods.- <I>C. Hennig:</I> What Clusters Are Generated by Normal Mixtures?.- <I>S. Winsberg, G.</I> <I>deSoete:</I> A Bootstrap Procedure for Mixture Models.- <B>Fuzzy </B> <B>Clustering:</B> <I>A. Devillez, P. Billaudel, G. Villermain </I> <I>Lecolier:</I> A New Criterion of Classes Validity.- <I>A. Gillet, </I> <I>C. Botte-Lecocq, L. Macaire, J.-G. Postaire:</I> Application of Fuzzy Mathematical Morphology for Unsupervised Color Pixels Classification.- <I>N. Watanabe, T. Imaizumi, T. Kikuchi:</I> A Hyperbolic Fuzzy- k-Means Clustering and Algorithm for Neural Networks.- <B>Special Purpose Classification Procedures </B> <B>and Applications:</B> <I>P. Makagonov, M. Alexandrov, K. </I> <I>Sboychakov:</I> Toolkit for Development of the Domain-Oriented Dictionaries for Structuring Document Flows.- <I>D. Wishart:</I> Classification of Single Malt Whiskies.- <I>J.-P. Valois:</I> Robust Approach in Hierarchical Clustering: Application to the Sectorisation of an Oil Field.- <I>H. Vos:</I> A Minimax Solution for Sequential Classification Problems.- <B>Verification and Comparison of Clusterings:</B> <I>I. Pinto Doria, </I> <I>G. Le Calve, H. Bacelar-Nicolau:</I> Comparison of Ultrametrics Obtained with Real Data, Using the PL and VALaw Coefficients.- <I>P. Kuntz, F. Henaux:</I> Numerical Comparisons of two Spectral Decompositions for Vertex Clustering.- <I>C. </I> <I>Soares, P. Brazdil, J. Costa:</I> Measures to Evaluate Rankings of Classification Algorithms.- <I>G. Cucumel, F.-J. Lapointe:</I> A General Approach to Test the Pertinence of a Consensus Classification.- <B>Dissimilarity Measures:</B> <I>F. Bavaud:</I> On a Class of Aggregation-invariant Dissimilarities Obeying the Weak Huygens` Principle.- <I>B. Fichet:</I> A Short Optimal Way for Constructing Quasi-ultrametrics From Some Particular Dissimilarities.- <B>Missing Data in Cluster Analysis:</B> <I>A. </I> <I>Guénoche, S. Grandcolas:</I> Estimating Missing Values in a Tree Distance.- <I>C. Levasseur, P.-A. Landry, F.-J. Lapointe:</I> Estimating Trees From Incomplete Distance Matrices: A Comparison of Two Methods.- <I>J. Martín-Fernández, C. </I> <I>Barceló-Vidal, V. Pawlowsky-Glahn:</I> Zero Replacement in Compositional Data Sets.- <I>C. Ambroise, G. Govaert:</I> EM Algorithm for Partially Known Labels.- <B>Discrimination, </B> <B>Regression Trees, and Data Mining:</B> <B>Discriment Analysis:</B> <I>M. </I> <I>Bardos:</I> Detection of Company Failure and Global Risk Forecasting.- <I>I. Brito, G. Celeux:</I> Discriminant Analysis by Hierarchical Coupling in EDDA Context.- <I>A. Ferreira, G. </I> <I>Celeux, H. Bacelar-Nicolau:</I> Discrete Discriminant Analysis: The Performance of Combining Models by a Hierarchical Coupling Approach.- <I>H. Chamlal, S. Slaoui Chah:</I> Discrimination Based on the Atypicity Index versus Density Function Ratio.- <B>Decision and Regression Trees:</B> <I>C. Cappeli, </I> <I>F. Mola, R. Siciliano:</I> A Third Stage in Regression Tree Growing: Searching for Statistical Reliability.- <I>J. Chauchat, R. Rakotomalala:</I> A New Sampling Strategy for Building Decision Trees from Large Databases.- <I>C. Conversano, F. Mola, R. Siciliano:</I> Generalized Additive Multi-Model for Classification and Prediction.- <I>R. Miglio, </I> <I>M. Pillati:</I> Radial Basis Function Networks and Decision Trees in the Determination of a Classifier.- <I>L. Torgo, J. </I> <I>Pinto da Costa:</I> Clustered Multiple Regression.- <B>Neutral </B> <B>Networks and Data Mining:</B> <I>A. Ciampi, Y. Lechevallier:</I> Constructing Artificial Neural Networks for Censored Survival Data, Statistical Models.- <I>A. Ultsch:</I> Visualisation and Classification with Artificial Life.- <B>Pattern Recognition </B> <B>and Geometrical Statistics:</B> <I>G. Porzio, G. Ragozini:</I> Exploring the Periphery of Data Scatters: Are There Outliers?.- <I>M. Rémon:</I> Discriminant Analysis Tools for Non Convex Pattern Recognition.- <I>A. Sbihi, A. Moussa, B. </I> <I>Benmiloud, J.-G. Postaire:</I> A Markovian Approach to Unsupervised Multidimensional Pattern Classification.- <B>Multivariate and Multidimensional Data Analysis: </B> <B>Multivariate Data Analysis:</B> <I>M. Mizuta, H. Minami:</I> An Algorithm with Projection Pursuit for Sliced Inverse Regression Model.- <I>W. Polasek, S. Liz:</I> Testing Constraints and Misspecification in VAR-ARCH Models.- <I>T. Rivas Moya:</I> Goodness of Fit Measure based on Sample Isotone Regression of Mokken Double Monotonicity Model.- <B>Multiway Data </B> <B>Analysis:</B> <I>R. Coppi, P. D`Urso:</I> Fuzzy Time Arrays and Dissimilarity Measures for Fuzzy Time Trajectories.- <I>D. </I> <I>Vicari:</I> Three-Way Partial Correlation Measures.- <B>Analysis of</B> <B>Network and Relationship Data and Multidimensional Scaling:</B> <I>S. Wassermann, P. Pattison:</I> Statistical Models for Social Networks.- <I>J. Trejos, W. Castillo, J. González, M. </I> <I>Villalobos:</I> Application of Simulated Annealing in Some Multidimensional Scalig Problems.- <I>S. Bonnevay, C. </I> <I>Largeron-Leteno:</I> Data Analysis Based on Minimal Closed Subsets.- <B>Robust Multivariate Methods:</B><I>S. Van Aelst, K. Van </I> <I>Driessen, P. Rousseeuw:</I> A Robust Method for Multivariate Regression.- <I>U. Gather, C. Becker, S. Kuhnt:</I> Robust Methods for Complex Data Structures.- <I>C. Dehon, P. Filzmoser, C. </I> <I>Croux:</I> Robust Methods for Canonical Correlation Analysis.- <B><I>Data Science:</B> <B>Data Science and Data Collection:</B></I> <I>N. Ohsumi:</I> From Data Analysis to Data Science.- <I>C. Hayashi:</I> Evaluation of Data Quality and Data Analysis.- <I>S. De Cantis, A. </I> <I>Oliveri:</I> Collapsibility ad Collapsing Multidimensional Contingency Tables - Perspectives and Implications.- <B>Sampling and Internet Surveys:</B> <I>V. Vehovar, K. Lozar </I> <I>Manfreda, Z. Batagelj:</I> Data Collected on the Web.- <I>O. </I> <I>Yoshimura, N. Ohsumi:</I> Some Experimental Surveys on the WWW Environments in Japan.- <I>A. Scagni:</I> Bootstrap Goodness-of-fit Tests for Complex Survey Samples.- <B>Symbolic Data Analysis:</B> <B>Classification and Analysis of Symbolic Data:</B> <I>L. Billard, E.</I> <I>Diday:</I> Regression Analysis for Interval-Valued Data.- <I>F. de </I> <I>Carvalho, C. Anselmo, R. de Souza:</I> Symbolic Approach to Classify Large Data Sets.- <I>N. Lauro, R. Verde, F. Palumbo:</I> Factorial Methods with Cohesion Constraints on Symbolic Objects.- <I>R. Verde, F. de Carvalho, Y. Lechevalier:</I> A Dynamical Clustering Algorithm for Multi-nominal Data.- <B>Software:</B> <I>G. Hébrail, Y. Lechevalier:</I> DB2SO: A Software for Building Symbolic Objects from Databases.- <I>R. Bisdorff, E. </I> <I>Diday:</I> Symbolic Data Analysis and the SODAS Software in Official Statistics.- <I>M. Bravo:</I> Strata Decision Tree SDA Software.- <I>M. Gettler Summa:</I> Marking and Generalization by Symbolic Objects in the Symbolic Official Data Analysis Software.