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:
http://www.cs.uvm.edu/~icdm/

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