## Classification and Data Analysis: Theory and Application Proceedings of the Biannual Meeting of the Classification Group of Societą Italiana di Statistica (SIS) Pescara, July 3–4, 1997International 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 -GfKl; Japanese Classification Society -JCS; Classification Group of Italian Statistical Society - COSIS; Societe Francophone de Classification -SFC. Now the IFCS includes the following Societies: Dutch-Belgian Classification Society - VOC; Polish Classification Section - SKAD; Portuguese Classification Association - CLAD; Group-at-Large; Korean Classification Society -KCS. Biannual Meeting of the Classification and Data Analysis Group of SIS The biannual meeting of the Classification and Data Analysis Group of Societa Italiana di Statistica (SIS) was held in Pescara, July 3 -4, 1997. The 69 papers presented were divided in 17 sessions. Each session was organized by a chairperson with two invited speakers and two contributed papers from a call for papers. All the works were referred. Furthermore, during the meeting a discussant was provided for each session. A short version of the papers (4 pages) was.published before the conference. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

11 | |

Forecasting a classification | 27 |

an example of spatiotemporal analysis 43 | 42 |

Variable selection in fuzzy clustering | 63 |

Discretization of continuousvalued data in symbolic | 81 |

Logistic discrimination by KullbackLeibler type distance measures | 89 |

Notes on methods for improving unstable classifiers | 105 |

Latent budget trees for multiple classification | 121 |

On the assessment of geographical survey units using | 221 |

Kalman filter applied to noncausal models for spatial data | 229 |

A paradigmatic path for statistical content analysis using | 237 |

The analysis ofauxological data by means ofnonlinear | 247 |

Threeway data arrays with double neighborhood relations | 263 |

Firm performance analysis with panel data | 271 |

Detection of multivariate outliersby convex hulls | 279 |

some observations | 295 |

Comparison of Euclidean approximations of nonEuclidean distances | 139 |

Professional positioning based on dominant eigenvalue scores DES | 155 |

Dynamic factor analysis | 171 |

Principal surfaces constrained analysis | 187 |

Generalised canonical analysis on symbolic objects | 195 |

Analysis of qualitative variables in structural models with unique solutions | 203 |

line transect data | 211 |

Projection pursuit regression with mixed variables | 303 |

Kernel methods for estimating covariance functions from curves | 319 |

Asymptotic prior to posterior analysis for graphical gaussian models | 335 |

A new approach to the stock location assignment problem | 353 |

a PC system for binary and ternary segmentation analysis | 367 |

### Other editions - View all

### Common terms and phrases

applied approach bias binary bootstrap classification cluster analysis clustering algorithm coefficient computed considered convex hull correlation corresponding covariance function covariance matrix criterion cross-validation Data Analysis data set defined denote density estimation diagonal dimensions dissimilarity distance distribution eigenvalues eigenvectors error Euclidean evaluation example factorial Figure forecasting fuzzy given graph graphical i-th Kalman filter kernel Kronecker product least squares linear mean measure method MSRF Multidimensional Scaling multiple multivariate neighbourhood neural network node observations obtained optimal outliers paper parameters partition performance plot points polychoric predictors Principal Component Analysis principal components problem procedure Projection Pursuit proposed Rand index random recursive References regression function representation represented response variable sample selection simulated smoother smoothing solution spatial Statistica statistical step structure symbolic data symbolic objects Table technique telephone exchange areas training set units Universitą values variance variogram vector weight