• Theoretic foundations
  • Novel models and algorithms
  • Association analysis
  • Clustering
  • Classification
  • Statistical methods for data mining
  • Data pre-processing
  • Feature extraction and selection
  • Post-processing including quality assessment and validation
  • Mining heterogeneous/multi-source data
  • Mining sequential data
  • Mining spatial and temporal data
  • Mining unstructured and semi-structured data
  • Mining graph and network data
  • Mining social networks
  • Mining high dimensional data
  • Mining uncertain data
  • Mining imbalanced data
  • Mining dynamic/streaming data
  • Mining behavioral data
  • Mining multimedia data
  • Mining scientific data
  • Privacy preserving data mining
  • Anomaly detection
  • Fraud and risk analysis
  • Security and intrusion detection
  • Visual data mining
  • Interactive and online mining
  • Ubiquitous knowledge discovery and agent-based data mining
  • Integration of data warehousing, OLAP and data mining
  • Parallel, distributed, and cloud-based high performance data miningmining
  • Opinion mining and sentiment analysis
  • Human, domain, organizational and social factors in data mining
  • Applications to healthcare, bioinformatics, computational chemistry, finance, eco-informatics, marketing, gaming, etc