Topics of Interest

Data mining foundations

Novel data mining algorithms in traditional areas (such as classification, regression, clustering, probabilistic modeling, pattern discovery, and association analysis)
Models and 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
Distributed data mining
High performance implementations of data mining algorithms
Privacy and anonymity-preserving data analysis

Stream Data Mining
Mining moving object data, RFID data, and data from sensor networks
Ubiquitous knowledge discovery
Mining multi-agent data and agent-based data mining
Mining and link analysis in networked settings: web, social and computer networks, and online communities
Mining the semantic web
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
Integration of data warehousing, OLAP and data mining
Data mining query languages
Security and data integrity