TAMIDA 20/21

X Symposium of Theory and Applications of Data Mining

This symposium provides a forum for researchers seeking to extract useful knowledge from large volumes of information using Data Mining methods. Data Mining techniques have become a fundamental tool in scientific and technical research as a method of analysis and knowledge discovery based on observation data or experimental results for decision making in the control of productive processes, systems planning, marketing, etc. The participation of the Spanish Network of Data Mining and Learning in the XIX Conference of the Spanish Association for Artificial Intelligence CAEPIA 20/21 aims to exchange ideas and knowledge that will contribute to the advancement of this line of research and enable its transfer to society.

Chairs

Alicia Troncoso, Universidad Pablo de Olavide.
José C. Riquelme, Universidad de Sevilla.

Topics

Topics of interest include, but are not limited to the following:

• AUTOMATIC LEARNING ALGORITHMS AND TECHNIQUES
• Feature selection
• Classification
• Clustering
• Parametric and non-parametric statistical techniques
• Soft-computing
• Discovery of rules
• Instances based
• Scalable algorithms
• Incremental algorithms
• Ensembles
• PATTERN RECOGNITION
• DEEP LEARNING
• LEARNING ASSESSMENT
• Design of experiments
• Statistical tests
• DATA PRE-PROCESSING
• Data reduction and/or transformation
• Uncertainty and incomplete data
• Discretion
• Unbalanced data
• MINING ON DIFFERENT TYPES OF DATA
• Texts
• Temporary
• Space
• Flows (streams)
• Networks
• Multimedia
• ADVANCED LEARNING PARADIGMS IN DATA MINING
• Unbalanced learning
• Multi-instance Learning
• Multi-label and multi-target
• Prediction with ordinal outputs
• Semi-supervised or weakly supervised learning
• Cluster Discovery and Exception Mining
• BIG DATA MINING
• VISUAL DATA MINING
• DATABASES AND DATA MINING
• OLAP
• Data Mining query
• Relational Data Mining
• DATA MINING APPLICATIONS
• Biomedical databases
• Industrial production processes
• Environmental problems
• Financial or commercial problems
• Web Mining
• Social Mining

Program Committee