• Data mining foundations
  
  • Novel data mining algorithms in traditional areas (such as classification,     regression, clustering, probabilistic modeling, and association analysis)
  • Algorithms for new, structured, data types, such as arising in chemistry,      biology, environment, and other scientific domains
  • Developing a unifying theory of data mining
  • Mining sequences and sequential data
  • Mining spatial and temporal datasets
  • Mining textual and unstructured datasets
  • High performance implementations of data mining algorithms
    
•   Mining in targeted application contexts
  
  • Mining high speed data streams
  • Mining sensor data
  • Distributed data mining and mining multi-agent data
  • Mining in networked settings: web, social and computer networks, and online communities
  • Data mining in electronic commerce, such as recommendation,sponsored web search, advertising, and marketing tasks
    
•   Methodological aspects and the KDD process
  
  • Data pre-processing, data reduction, feature selection, and feature    transformation
  • Quality assessment, interestingness analysis, and post-processing
  • Statistical foundations for robust and scalable data mining
  • Handling imbalanced data
  • Automating the mining process and other process related issues
  • Dealing with cost sensitive data and loss models
  • Human-machine interaction and visual data mining
  • Security, privacy, and data integrity
    
• Integrated KDD applications and systems
  
  • Bioinformatics, computational chemistry, geoinformatics, and other     science & engineering disciplines
  • Computational finance, online trading, and analysis of markets
  • Intrusion detection, fraud prevention, and surveillance
  • Healthcare, epidemic modeling, and clinical research
  • Customer relationship management
  • Telecommunications, network and systems management