-
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.
-
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. Social Networks
D19. Functional Data Analysis
D20. Big Data Analysis
D21. 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.