ICDM Topics of Interest

Topics related to the design, analysis and implementation of data mining theory, systems and applications are of interest. These include, but are not limited to the following areas:

• 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