CLADAG 2009
Seventh Scientific Meeting of the
CLAssification and Data Analysis Group
of the Italian Statistical Society
Cladag     
Università di Catania (Italy) - September 9-11, 2009

About CLADAG

The Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS) promotes advanced methodological research in multivariate statistics with a special interest in Data Analysis and Classification. CLADAG supports the interchange of ideas in these fields of research, including the dissemination of concepts, numerical methods, algorithms, computational and applied results. CLADAG is a member of the International Federation of Classification Societies (IFCS). Among its activities, CLADAG organizes a biennial international scientific meeting, schools related to classification and data analysis, publishes a newsletter, and cooperates with other member societies of the IFCS in the organization of their conferences. Founded in 1985, the IFCS is a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research.

The scientific meetings of the CLAssification and Data Analysis Group of the Italian Statistical Society take place under the auspices of the International Federation of Classification Societies and of the Italian Statistical Society. Previous CLADAG meetings were held in Pescara (1997), Roma (1999), Palermo (2001), Bologna (2003), Parma (2005) and Macerata (2007).

Conference Themes

Classification Theory T1. Fuzzy Methods – T2. Hierarchical Classification – T3. Non Hierarchical Classification – T4. Pattern Recognition – T5.Bayesian Classification – T6. Classification of Multiway and Functional Data – T7. Probabilistic Methods for Clustering – T8. Consensus of Classifications – T9. Spatial Clustering – T10. Validity of Clustering – T11. Neural Networks and Machine Learning Methods – T12. Genetic Algorithms – T13. Classification with Constraints – T14. Latent Class Models for Clustering.

Multivariate Data Analysis D1. Categorical Data Analysis – D2. Correspondence Analysis – D3. Biplots – D4. Factor Analysis and Dimension Reduction Methods – D5. Discrimination and Classification – D6. Multiway Methods – D7. Symbolic Data Analysis – D8. Non Linear Data Analysis – D9. Mixture Models – D10. Multilevel Analysis – D11. Covariance Structure Analysis – D12. Partial Least Squares – D13. Regression and Classification Trees – D14. Robust Methods and Data Diagnostics – D15. Spatial Data Analysis – D16. Item Response Theory – D17. Nonparametric and Semiparametric Regression – D18. Data Mining.

Proximity Structure Analysis P1. Multidimensional Scaling – P2. Similarities and Dissimilarities – P3. Unfolding and Other Special Scaling Methods – P4. Multiway Scaling.

Software Developments S1. Algorithms for Classification – S2. Data Visualization – S3. Algorithms for Multivariate Data Analysis.

Applied Classification and Data Analysis A1. Classification of Textual Data – A2. Data Analysis in Economics and Finance – A3. Data Analysis in Environmental Sciences – A4 Classification in Medical Science – A5. Cognitive Sciences and Classification – A6. Classification in Biology and Ecology – A7. Data Analysis in Demography – A8. Classification of Microarray Data – A9. Data Analysis for Customer Satisfaction and Service Quality Evaluation – A10. Applications of Data and Web Mining.