Overview

The volume presents new developments in data analysis and classification, and gives a state of the art impression of these scientific fields at the turn of the Millennium. Areas that receive considerable attention in this book are Cluster Analysis, Data Mining, Multidimensional and Symbolic Data Analysis, Decision and Regression Trees. The volume contains a refereed selection of original research papers, overview papers, and innovative applications presented at the 7th Conference of the International Federation of Classification Societies (IFCS-2000), with contributions from eminent scientists all over the world. The reader finds introductory material into various areas and kaleidoscopic views of recent technical and methodological developments in widely different areas within data analysis and classification. The presence of a large number of application papers demonstrates the usefulness of the recently developed techniques.

Full Product Details

Author:   Henk A.L. Kiers ,  Jean-Paul Rasson ,  Patrick J.F. Groenen ,  Martin Schader
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   Softcover reprint of the original 1st ed. 2000
Dimensions:   Width: 15.50cm , Height: 2.20cm , Length: 23.50cm
Weight:   0.670kg
ISBN:  

9783540675211

ISBN 10:   3540675213
Pages:   428
Publication Date:   21 June 2000
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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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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