The conference language is English.

Template

For presenting a paper at the conference, fill the preregistration form and enter the submission area; please submit an abstract of maximum 2 pages.
Use the LaTeX abstract template (downloadable from here) and upload a .zip file cointaining the .tex file, any image files and the .pdf version of your abstract in the submission area.

Download the extended paper submission template (downloadable from here)

List of topics for the autoclassification of the contributed papers:

Classification Theory
T01. Fuzzy Methods - T02. Hierarchical Classification - T03. Non Hierarchical Classification - T04. Pattern Recognition - T05. Bayesian Classification - T06. Classification of Multi-way and Functional Data - T07. Probabilistic Methods for Clustering - T08. Consensus of Classifications - T09. 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
D01. Categorical Data Analysis - D02. Correspondence Analysis - D03. Bi-plots - D04. Factor Analysis and Dimension Reduction Methods - D05. Discrimination and Classification - D06. Multi-way Methods - D07. Symbolic Data Analysis - D08. Non Linear Data Analysis - D09. Bayesian Multivariate Analysis - 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. Graphical Methods - D17. Nonparametric and Semi-parametric Regression - D18. Causal Analysis - D19. Data Mining and Knowledge Discovery

Proximity Structure Analysis
P01. Multidimensional Scaling - P02. Similarities and Dissimilarities - P03. Unfolding and Other Special Scaling Methods - P04. Multi‐way Scaling

Software Developments
S01. Algorithms for Classification - S02. Data Visualization - S03. Algorithms for Multivariate Data Analysis

Applied Classification and Data Analysis
A01. Classification of Textual Data - A02. Data Analysis in Business Administration, Economics and Finance - A03. Data Analysis in Environmental Sciences - A04. Classification in Medical Science - A05. Cognitive Sciences and Classification - A06. Classification in Biology and Ecology - A07. Data Analysis in Demography - A08. Classification of Microarray Data - A09. Data Analysis for Customer Satisfaction and Service Quality Evaluation - A10. Applications of Data and Web Mining - A11. Business Intelligence - A12. Geospatial Data Analysis - A13. Classification in Signal Analysis