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

Data Mining Foundations

• Parallel and Distributed Data Mining Algorithms,

• Data Streams Mining, Graph Mining,

• Spatial Data Mining, Text Video,

• Multimedia Data Mining, Web Mining,

• Pre-processing Techniques, Visualization,

• Security and Information Hiding in Data Mining.

Data Mining Applications

• Databases, Bioinformatics,

• Biometrics,

• Image Analysis,

• Financial Modeling,

• Forecasting, Classification,

• Clustering,

• Social Networks,

• Educational Data Mining.

Knowledge Processing

• Data and Knowledge Representation

• Knowledge Discovery Framework and Process,

• Including Pre- and Post-processing,

• Integration of Data Warehousing,

• OLAP and Data Mining,

• Integrating Constraints and Knowledge in the KDD Process ,

• Exploring Data Analysis, Inference of Causes,

• Prediction, Evaluating,

• Consolidating, and Explaining Discovered Knowledge,

• Statistical Techniques for Generation a Robust,

• Consistent Data Model,

• Interactive Data Exploration/visualization and Discovery,

• Languages and Interfaces for Data Mining,

• Mining Trends,

• Opportunities and Risks,

• Mining from Low-Quality Information Sources.