Recent Developments in Clustering and Data Analysis

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.

Contributors

Preface

Section 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 Analysis

Section 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 Substances

Section 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 Valu