Topics of Interest

• 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 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

• 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

• 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