Classification, Clustering, and Data Analysis: Recent Advances and Applications (Studies in Classification, Data Analysis, and Knowledge Organization)

Springer, 2002. — 459 p. — ISBN: 978-3-540-43691-1, e-ISBN: 978-3-642-56181-8. Series: Studies in Classification, Data Analysis, and Knowledge Organization. The book presents a long list of useful methods for classification, clustering and data analysis. By combining theoretical aspects with practical problems, it is designed for researchers as well as for applied statisticians and will support the fast transfer of new methodological advances to a wide range of applications. Contents: Part I. Clustering and Discrimination . Clustering . Some Thoughts about Classification. Partial Defuzzification of Fuzzy Clusters. A New Clustering Approach, Based on the Estimation of the Probability Density Function, for Gene Expression Data. Two-mode Partitioning: Review of Methods and Application of Tabu Search. Dynamical Clustering of Interval Data Optimization of an Adequacy Criterion Based on Hausdorff Distance. Removing Separation Conditions in a 1 against 3-Components Gaussian Mixture Problem. Obtaining Partitions of a Set of Hard or Fuzzy Partitions. Clustering for Prototype Selection using Singular Value Decomposition. Clustering in High-dimensional Data Spaces. Quantization of Models: Local Approach and Asymptotically Optimal Partitions. The Performance of an Autonomous Clustering Technique. Cluster Analysis with Restricted Random Walks. Missing Data in Hierarchical Classification of Variables — a Simulation Study. Cluster Validation . Representation and Evaluation of Partitions. Assessing the Number of Clusters of the Latent Class Model. Validation of Very Large Data Sets Clustering by Means of a Nonparametric Linear Criterion. Discrimination . Effect of Feature Selection on Bagging Classifiers Based on Kernel Density Estimators. Biplot Methodology for Discriminant Analysis Based upon Robust Methods and Principal Curves. Bagging Combined Classifiers. Application of Bayesian Decision Theory to Constrained Classification Networks. Part II. Multivariate Data Analysis and Statistics . Multivariate Data Analysis . Quotient Dissimilarities, Euclidean Embeddability, and Huygens' Weak Principle Conjoint Analysis and Stimulus Presentation — a Comparison of Alternative Methods. Grade Correspondence-cluster Analysis Applied to Separate Components of Reversely Regular Mixtures. Obtaining Reducts with a Genetic Algorithm. A Projection Algorithm for Regression with Collinearity. Confronting Data Analysis with Constructivist Philosophy. Statistical Methods . Maximum Likelihood Clustering with Outliers. An Improved Method for Estimating the Modes of the Probability Density Function and the Number of Classes for PDF-based Clustering. Maximization of Measure of Allowable Sample Sizes Region in Stratified Sampling. On Estimation of Population Averages on the Basis of Cluster Sample. Symbolic Data Analysis . Symbolic Regression Analysis. Modelling Memory Requirement with Normal Symbolic Form. Mixture Decomposition of Distributions by Copulas. Determination of the Number of Clusters for Symbolic Objects Described by Interval Variables. Symbolic Data Analysis Approach to Clustering Large Datasets. Symbolic Class Descriptions. Consensus Trees and Phylogenetics. A Comparison of Alternative Methods for Detecting Reticulation Events in Phylogenetic Analysis. Hierarchical Clustering of Multiple Decision Trees. Multiple Consensus Trees. A Family of Average Consensus Methods for Weighted Trees. Comparison of Four methods for Inferring Additive Trees from Incomplete Dissimilarity Matrices. Quartet Trees as a Tool to Reconstruct Large Trees from Sequences. Regression Trees . Regression Trees for Longitudinal Data with Time-dependent Covariates. Tree-based Models in Statistics: Three Decades of Research. Computationally Efficient Linear Regression Trees. Neural Networks and Genetic Algorithms . A Clustering Based Procedure for Learning the Hidden Unit Parameters in Elliptical Basis Function Networks. Multi-layer Perceptron on Interval Data. Part III. Applications . Textual Analysis of Customer Statements for Quality Control and Help Desk Support. AHP as Support for Strategy Decision Making in Banking. Bioinformatics and Classification: The Analysis of Genome Expression Data. Glaucoma Diagnosis by Indirect Classifiers. A Cluster Analysis of the Importance of Country and Sector on Company Returns. Problems of Classification in Investigative Psychology. Springer, 2002. — 459 p. — ISBN: 978-3-540-43691-1, e-ISBN: 978-3-642-56181-8.Series: Studies in Classification, Data Analysis, and Knowledge Organization. The book presents a long list of useful methods for classification, clustering and data analysis. By combining theoretical aspects with practical problems, it is designed for researchers as well as for applied statisticians and will support the fast transfer of new methodological advances to a wide range of applications. __**Contents:**____Part I. Clustering and Discrimination__.**Clustering**.Some Thoughts about Classification.Partial Defuzzification of Fuzzy Clusters.A New Clustering Approach, Based on the Estimation of the Probability Density Function, for Gene Expression Data.Two-mode Partitioning: Review of Methods and Application of Tabu Search.Dynamical Clustering of Interval Data Optimization of an Adequacy Criterion Based on Hausdorff Distance.Removing Separation Conditions in a 1 against 3-Components Gaussian Mixture Problem.Obtaining Partitions of a Set of Hard or Fuzzy Partitions.Clustering for Prototype Selection using Singular Value Decomposition.Clustering in High-dimensional Data Spaces.Quantization of Models: Local Approach and Asymptotically Optimal Partitions.The Performance of an Autonomous Clustering Technique.Cluster Analysis with Restricted Random Walks.Missing Data in Hierarchical Classification of Variables — a Simulation Study.**Cluster Validation**.Representation and Evaluation of Partitions.Assessing the Number of Clusters of the Latent Class Model.Validation of Very Large Data Sets Clustering by Means of a Nonparametric Linear Criterion.**Discrimination**.Effect of Feature Selection on Bagging Classifiers Based on Kernel Density Estimators.Biplot Methodology for Discriminant Analysis Based upon Robust Methods and Principal Curves.Bagging Combined Classifiers.Application of Bayesian Decision Theory to Constrained Classification Networks. __Part II. Multivariate Data Analysis and Statistics__.**Multivariate Data Analysis**.Quotient Dissimilarities, Euclidean Embeddability, and Huygens' Weak PrincipleConjoint Analysis and Stimulus Presentation — a Comparison of Alternative Methods.Grade Correspondence-cluster Analysis Applied to Separate Components of Reversely Regular Mixtures.Obtaining Reducts with a Genetic Algorithm.A Projection Algorithm for Regression with Collinearity.Confronting Data Analysis with Constructivist Philosophy.**Statistical Methods**.Maximum Likelihood Clustering with Outliers.An Improved Method for Estimating the Modes of the Probability Density Function andthe Number of Classes for PDF-based Clustering.Maximization of Measure of Allowable Sample Sizes Region in Stratified Sampling.On Estimation of Population Averages on the Basis of Cluster Sample.**Symbolic Data Analysis**.Symbolic Regression Analysis.Modelling Memory Requirement with Normal Symbolic Form.Mixture Decomposition of Distributions by Copulas.Determination of the Number of Clusters for Symbolic Objects Described by Interval Variables.Symbolic Data Analysis Approach to Clustering Large Datasets.Symbolic Class Descriptions.Consensus Trees and Phylogenetics.A Comparison of Alternative Methods for Detecting Reticulation Events in Phylogenetic Analysis.Hierarchical Clustering of Multiple Decision Trees.Multiple Consensus Trees.A Family of Average Consensus Methods for Weighted Trees.Comparison of Four methods for Inferring Additive Trees from Incomplete Dissimilarity Matrices.Quartet Trees as a Tool to Reconstruct Large Trees from Sequences.**Regression Trees**.Regression Trees for Longitudinal Data with Time-dependent Covariates.Tree-based Models in Statistics: Three Decades of Research.Computationally Efficient Linear Regression Trees.**Neural Networks and Genetic Algorithms**.A Clustering Based Procedure for Learning the Hidden Unit Parameters in Elliptical Basis Function Networks.Multi-layer Perceptron on Interval Data. __Part III. Applications__.Textual Analysis of Customer Statements for Quality Control and Help Desk Support.AHP as Support for Strategy Decision Making in Banking.Bioinformatics and Classification: The Analysis of Genome Expression Data.Glaucoma Diagnosis by Indirect Classifiers.A Cluster Analysis of the Importance of Country and Sector on Company Returns.Problems of Classification in Investigative Psychology. The present volume contains a selection of papers presented at the Eighth Conference of the International Federation of Classification Societies (IFCS) which was held in Cracow, Poland, July 16-19, 2002. All originally submitted papers were subject to a reviewing process by two independent referees, a procedure which resulted in the selection of the 53 articles presented in this volume. These articles relate to theoretical investigations as well as to practical applications and cover a wide range of topics in the broad domain of classifi­ cation, data analysis and related methods. If we try to classify the wealth of problems, methods and approaches into some representative (partially over­ lapping) groups, we find in particular the following areas: • Clustering • Cluster validation • Discrimination • Multivariate data analysis • Statistical methods • Symbolic data analysis • Consensus trees and phylogeny • Regression trees • Neural networks and genetic algorithms • Applications in economics, medicine, biology, and psychology. Given the international orientation of IFCS conferences and the leading role of IFCS in the scientific world of classification, clustering and data anal­ ysis, this volume collects a representative selection of current research and modern applications in this field and serves as an up-to-date information source for statisticians, data analysts, data mining specialists and computer scientists.

This book deals with recent developments in classification and data analysis and presents new topics which are of central interest to modern statistics. In particular, these include: classification models and clustering methods, multivariate data analysis, symbolic data, neural networks and learning devices, phylogeny and bioinformatics, new software systems for classification and data analysis, as well as applications in social, economic, biological, medical and other sciences. The book presents a long list of useful methods for classification, clustering and data analysis. By combining theoretical aspects with practical problems it is designed for researchers as well as for applied statisticians and will support the fast transfer of new methodological advances to a wide range of applications.

Abstract. The paper contains some general remarks on the high art of data analysis, some philosophical thoughts about classification, a partial review of outliers and robustness from the point of view of applications, including a discussion of the problem of model choice, and a review of several aspects of robust estimation of covariance matrices, including the pragmatic choice of a weight function based on empirical and theoretical evidence. Dealing with developments in classification and data analysis, this book presents topics of interest to modern statistics, including classification models and clustering methods, multi-variate data analysis, symbolic data, neural networks and learning devices, phylogeny, bioinformatics and more