Recent Developments in Clustering and Data Analysis presents the results of clustering and multidimensional data analysis research conducted primarily in Japan and France. This book focuses on the significance of the data itself and on the informatics of the data.Organized into four sections encompassing 35 chapters, this book begins with an overview of the quantification of qualitative data as a method of analyzing statistically multidimensional data. This text then examines the rules of interpretation of correspondence cluster analysis by selecting classes and explaining variables involved in the algorithm of hierarchical classification. Other chapters consider the bootstrap and cross-validation methods, which are applied to the logistic ad nonparametric regression analyses of ordered categorical responses. The final chapter deals with a simpler treatment to classify the sleep state.This book is a valuable resource for researchers and workers in the fields from the behavioral sciences, biological sciences, medicine, and industrial sciences.

¿ContributorsPrefaceSection 1 Data Analysis Techniques and Related Topics with Statistical Software New Developments in Multidimensional Data Analysis Interpretation of Some Data Analysis Methods A General Euclidean Approach for Measuring and Describing Associations Between Several Sets of Variables Data-Adaptive Methods in Multivariate Analysis Specific Design of a Software for Multivariate Descriptive Statistical Analysis: The Case of SPAD.N A Test of Goodness of Fit Based on Graphical Method Graphical Analysis of Ranks A Unified Study of Multivariate Data Analysis Methods by Nonlinear Formulations and Underlying Probabilistic Structures Optimum Classification Boundaries Based on a Concomitant Variable in Sample Survey: Some Application to the Current Statistics of Commerce On the Identification Problem of Age-Period-Cohort AnalysisSection 2 Automatic Classification and Related Techniques Some Recent Advances in Clustering Simultaneous Clustering of Cases amd Variances Techniques of Approximation for Building Two Tree Structures A Hierarchical Clustering Method for Dissimilarity Matrices with Individual Differences Applications of Multivariate and Cladistic Data Analyses for the Classification of Dermapterous Insects Comparing Relational Variables According to Likelihood of the Links Classification Method Role of Computer Graphics in Interpretation of Clustering Results Classification of Fruit and Vegetable Varieties by Chemical Analysis of Fragrance SubstancesSection 3 Scaling Method and Correspondence Analysis from the Viewpoint of Practical Approach Assessing the Number of Axes that Should Be Considered in Correspondence Analysis Singular Value Decomposition of Multiarray Data and Its Applications Partial Correspondence Analysis and Its Properties Correlation Analysis of N-Way Qualitative Data and Its Applications Outliers and Influential Observations in Quantification Theory Conversational Data Analysis System: Selection of Items and Pooling of Categories Analysis and Comparison of Different Tables Use of Tabulated Data in Data AnalysisSection 4 Applications: Extraction and Interpretation of Information in Multidimensional Data Mutivariate Descriptive Techniques Applied to the Processing of Longitudinal Sample Survey Data Multidimensional Analysis of Opinion Survey Data Data Analytic Approaches to Human Behavioral Relationships in Survey of Accidents About the Numerical and Statistical Stability of Forecasting Algorithms The Method of Pattern Classification and Its Application to Prognosis of Disease A Non Parametric Discriminant Analysis Based on the Construction of a Binary Decision Tree Experimental Comparison Between the Optimal Discriminate Plane Based on Samples and General Discriminant Analysis A Method of Discrimination for Electrical Biosignal Classification of Behavioral States of the Mouse