Data Analysis, Classification, and Related Methods

Inhaltsverzeichnis

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