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:

•	Foundations of data mining
•	Data mining and machine learning algorithms and methods in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis), and in new areas
•	Mining text and semi-structured data, and mining temporal, spatial and multimedia data
•	Mining data streams
•	Pattern recognition and trend analysis
•	Collaborative filtering/personalization
•	Data and knowledge representation for data mining
•	Query languages and user interfaces for mining
•	Complexity, efficiency, and scalability issues in data mining
•	Data pre-processing, data reduction, feature selection and feature transformation
•	Post-processing of data mining results
•	Statistics and probability in large-scale data mining
•	Soft computing (including neural networks, fuzzy logic, evolutionary computation, and rough sets) and uncertainty management for data mining
•	Integration of data warehousing, OLAP and data mining
•	Human-machine interaction and visual data mining
•	High performance and parallel/distributed data mining
•	Quality assessment and interestingness metrics of data mining results
•	Security, privacy and social impact of data mining
•	Data mining applications in bioinformatics, electronic commerce, Web, intrusion detection, finance, marketing, healthcare, telecommunications and other fields