Data analysis, classification, and related methods
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Bibliographic Information
Data analysis, classification, and related methods
（Studies in classification, data analysis, and knowledge organization）
Springer, c2000
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"A selection of papers presented at the Seventh Conference of the International Federation of Classification Societies (IFCS-2000) which was held in Namur, Belgium, July 11-14, 2000"--Pref
Includes bibliographical references and index
Description and Table of Contents
Description
This volume contains a selection of papers presented at the Seven~h Confer ence of the International Federation of Classification Societies (IFCS-2000), which was held in Namur, Belgium, July 11-14,2000. From the originally sub mitted papers, a careful review process involving two reviewers per paper, led to the selection of 65 papers that were considered suitable for publication in this book. The present book contains original research contributions, innovative ap plications and overview papers in various fields within data analysis, classifi cation, and related methods. Given the fast publication process, the research results are still up-to-date and coincide with their actual presentation at the IFCS-2000 conference. The topics captured are: * Cluster analysis * Comparison of clusterings * Fuzzy clustering * Discriminant analysis * Mixture models * Analysis of relationships data * Symbolic data analysis * Regression trees * Data mining and neural networks * Pattern recognition * Multivariate data analysis * Robust data analysis * Data science and sampling The IFCS (International Federation of Classification Societies) The IFCS promotes the dissemination of technical and scientific information data analysis, classification, related methods, and their applica concerning tions.
Table of Contents
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. Guenoche, 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. Martin-Fernandez, C. Barcelo-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. Remon: 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. Gonzalez, 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. Hebrail, 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.
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