2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)

Data analysis and data visualization

Temporal and incremental data mining

Various mathematical approaches including information theory and mathematical statistics

Software and hardware implementations

Architectural solutions including hierarchical and growing networks, ensemble models and special metrics

Neuro-cognitive studies that compare modeling and empirical results at different levels

Models, experimental investigations and applications of autonomous mental development.

We also call for scientific and practice-oriented papers that demonstrate the use of Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization approaches in different application areas including but not limited to :

Data mining

Pattern recognition

Signal processing

Knowledge management

Time series processing

Modeling dynamic phenomena

Anomalies and fault detection

Industrial applications

Bioinformatics

Biomedical applications

Telecommunications

Financial analysis

Applications for transport optimization

Cognitive modeling

Language modeling

Robotics and intelligent systems

Image processing and vision

Speech processing

Text and document analysis