Scientific Program

The conference will include a President’s invited session and a Presidential address, invited presentations, invited and contributed sessions, and poster sessions. 

Invited Speakers

  • Professor Tomoyuki Higuchi (The Institute of Statistical Mathematics, Japan)
    Smart simulation and smart experimental design
  • Professor Christian Hennig (University College London, UK)
    Decisions that are needed when using cluster analysis,and research that helps with making them
  • Professor Marieke E. Timmerman (University of Groningen, The Netherlands)
    Mixture modelling of multivariate and / or longitudinal data: Arriving at insightful representations
  • Professor Irini Moustaki (London School of Economics & Political Science, UK)
    An overview of hybrid latent variable models and how they can be used to address outliers, flexible distributions of latent variables and issues of data dimensionality
  • Associate Professor Cinzia Viroli (University of Bologna, Italy)
    Classification by Quantiles

Special Sessions

  • SP01: Changing Environment, New Challenges, New Responses
  • SP02: Classification Models in Finance and Business
  • SP03: Clustering and Recommendation Systems
  • SP04: Advances in Latent Variable Modeling
  • SP05: Challenges in Visualisation and Classification
  • SP06: Clustering with Mixture Models
  • SP07: Cluster Validation: New Developments and Issues
  • SP08: Methods of Data Analysis and Statistical Measures in the Social Sciences
  • SP09: Cluster Analysis and Quantification
  • SP10: Perspectives of Contingency Table Analysis
  • SP11: Robustness and Active Learning with Logistic Models
  • SP12: Advanced Techniques for Analyzing (Big) Multi-set/Multi-subject Data
  • SP13: Cluster Analysis Using non-Gaussian Mixtures
  • SP14: Functional Data Analysis
  • SP15: Leading-Edge Research of Clustering and its Applications
  • SP16:  (Tentative) Enumeration Algorithm and Data Science
  • SP17: Judgment and Decision Making
  • SP18: Social Implementation Based on Analytic Results by Latent Classificatioin Methods for Health Management
  • SP19: New tTpics Related to Classification Problems
  • SP20: Recent Problems of Big Data Handling in Official Statistics
  • SP21: Causal Inference and Related Topics
  • SP22: Bayesian Inference and Model Selection
  • SP23: Advances in Sparse Regression, Dimension Reduction and Related Computations
  • SP24: Analysis of Micro Official Statistics
  • SP25: The Cutting Edge of Biomedical  Big Data - from Bioinformatic Methodologies to Data Analysis
  • SP26: Classification and Representation for Non Metric and Symbolic Data
  • SP27: Analysis and Clustering of Complicated Data
  • SP28: Data Mining and Data Visualization
  • SP29: Regularization Methods
  • SP30: Bayesian Approach to Classification 
  • SP31: (Tentative) Text Classification
  • SP32: Portuguese Association for Classification and Data Analysis (CLAD)
  • SP33: (Tentative) Clustering and Classification in Innovative Data Science
  • SP34: Survey Data Analysis
  • SP35: Marketing Science
  • SP36: Data Science, Classification and Clustering
  • SP37: Statistical Issues on Clustering and Classification in Medical Data Analysis
  • SP38: Issues in Classification with Complex Data Structures (CLADAG)