Advances in classification and data analysis
著者
書誌事項
Advances in classification and data analysis
（Studies in classification, data analysis, and knowledge organization）
Springer, c2001
大学図書館所蔵 件 / 全20件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references and index
内容説明・目次
内容説明
This volume contains a selection of papers presented at the biannual meeting of the Classification and Data Analysis Group of Societa Italiana di Statistica, which was held in Rome, July 5-6, 1999. From the originally submitted papers, a careful review process led to the selection of 45 papers presented in four parts as follows: CLASSIFICATION AND MULTIDIMENSIONAL SCALING Cluster analysis Discriminant analysis Proximity structures analysis and Multidimensional Scaling Genetic algorithms and neural networks MUL TIV ARIA TE DATA ANALYSIS Factorial methods Textual data analysis Regression Models for Data Analysis Nonparametric methods SPATIAL AND TIME SERIES DATA ANALYSIS Time series analysis Spatial data analysis CASE STUDIES INTERNATIONAL FEDERATION OF CLASSIFICATION SOCIETIES The International Federation of Classification Societies (IFCS) is an agency for the dissemination of technical and scientific information concerning classification and data analysis in the broad sense and in as wide a range of applications as possible; founded in 1985 in Cambridge (UK) from the following Scientific Societies and Groups: British Classification Society -BCS; Classification Society of North America - CSNA; Gesellschaft fUr Klassifikation - GfKI; Japanese Classification Society -JCS; Classification Group of Italian Statistical Society - CGSIS; Societe Francophone de Classification -SFC. Now the IFCS includes also the following Societies: Dutch-Belgian Classification Society - VOC; Polish Classification Society -SKAD; Associayao Portuguesa de Classificayao e Analise de Dados -CLAD; Korean Classification Society -KCS; Group-at-Large.
目次
- Part I. Classification and Multidimensional Scaling: Cluster Analysis.- D. Bruzzese, A. Irpino: Galois Lattices of Modal Symbolic Objects
- A. Cerioli, S. Zani: Exploratory Methods for Detecting High Densitiy Regions in Cluster Analysis
- G.D. Costanzo: A k-means Consensus Classification
- A. Di Ciaccio: MIXISO: A Non-Hierarchical Clustering Method for Mixed-Mode Data
- R. Piccarreta: 'Stable Clusters': A New Approach for Clustering Binary Variables
- M. Vichi: Double k-means Clustering for Simultaneous Classification of Objects and Variables
- Discriminant Analysis.- F. Palumbo: Catagorial FDA Under Prospective Sampling Scheme: a Proposal for Variable Selection
- A. Petrucci, M. Pratesi: The Effect of Telephone Survey Design on Discriminant Analysis
- Proximity Structures Analysis and Multidimensional Scaling.- L. Bocci: A Dissimilarity Measure between Probalistic Symbolic Objects
- S. Camiz, G. Le Calve: Recent Experimentation on Euclidean Approximations of Biased Euclidean Distances
- F. Cipollini, P. Ganugi: Comparing Capital Structure through Similarity Analysis: Evidence about two Industrial Districts
- R. Coppi, P. D'Urso: The Geometric Approach to the Comparison of Multivariate Time Trajectories
- D. Vicari: Ultramine Spaces in Classification
- Genetic Algorithms and Neural Networks.- R. Baragona, C. Calzini, F. Battaglia: Genetic Algorithms and Clustering: an Application to Fisher's Iris Data
- I. Morlini: Using Radial Basis Function Networks for Classification Problems
- R. Siciliano, A. Mooijaart: Unconditional Latent Budget Analysis: a Neural Network Approach
- Part II. Multivariate Data Analysis: Factorial Methods.- P. Amenta, L. D'Ambra: Generalized Constrained Principal Component Analysis
- R. Lombardo, J. van Rijckevorsel: Interaction Terms in Homogeneity Analysis: Higher Order Non-Linear Multiple Correspondence Analysis
- P. Mantovan, A. Pastore: Perturbation Models for Principal Component Analysis of Rainwater Pollution Data
- R. Rocci: Core Matrix Rotation to Natural Zeros in Three-Mode Factor Analysis
- Textual Data Analysis.- S. Balbi, G. Giordano: A Factorial Technique for Analysing Textual Data with External Information
- A. Tuzzi: Subjects on Using Open and Closed-Ended Questions
- Regression Models for Data Analysis.- R. Bernardini Papalia, F. Di Iorio: Alternative Error Term Specifications in the Log-Tobit Model
- G. D'Epifanio: A Customer Satisfaction Approach for User-Oriented Comparative Evaluations of Services
- F. Domma, S. Ingrassia: Mixture Models for Maximum Likelihood Estimation from Incomplete Values
- M. La Rocca: Robust Inference in the Logistic Regression Model
- G.C. Porzio: A plot for Submodel Selection in Generalized Linear Models
- G. Vittadini: On the Use of Multivariate Regression Models in the Context of Multilevel Analysis
- Nonparametric Methods.- R. Borgoni
- C. Provasi: Nonparametric Estimation Methods for Sparse Contingency Tables
- S. Borra, A. Di Ciaccio: Reduction of Prediction Error by Bagging Projection Pursuit Regression
- C. Cappelli, F. Mola, R. Siciliano: Selecting Regression Tree Models: a Statistical Testing Procedure
- P. D'Urso, T. Gastaldi
- Linear Fuzzy Regression Analysis with Asymmetric Spreads
- Part III. Spatial and Time Series Data Analysis: Time Series Analysis.- A. Amendola, F. Giordano, C. Perna: Forecasting Non-Linear Time Series: Empirical Evidences on Financial Data
- G. Cubadda, P. Daddi: Dynamics and Comovements of Regional Exports in Italy
- F. Giordano, C. Perna: Large-sample Properties of Neural Estimators in a Regression Model with (omega)-mixing errors
- M. La Rocca, C. Vitale: Subseries Lenght in MBB Procedure for (omega)-mixing Processes
- F. Moauro: Modelling a Change of Classification by a Structural Time Series Approach
- Spatial Data Analysis.- M. Alfo, P. Postiglione: Spatial Discriminant Analysis Using Covariates Information
- S. De Iaco, D. Posa: Some Aspect
「Nielsen BookData」 より
