Conference Themes

Contributed papers from scholars and practitioners are invited on any of the topics below as well as on related issues

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 for Classification – T12. Genetic Algorithms – T13. Classification with Constraints – T14. Mixture and Latent Class Models for Clustering

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

Data Analysis and Data Science

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. Functional Data Analysis D19. Data Mining – D20. High-dimensional data – D21. Deep Learning – D22. Machine Learning – D23. Statistical Learning

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 Data Analysis