The IEEE International Conference on Data Mining series (ICDM) has established itself as the world's premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of data mining, including algorithms, software and systems, and applications. In addition, ICDM draws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high performance computing. By promoting novel, high quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to continuously advance the state-of-the-art in data mining. Besides the technical program, the conference will feature workshops, tutorials, panels and, new for 2007, the ICDM data mining contest. 数据挖掘研究院
ICDM is held annually, in different regions of the world. The map below shows the locations and conference dates for ICDM 2001 to 2007. 数据挖掘实验室
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: 数据挖掘研究院
- 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
Steering Committee
The Steering Committee coordinates the conference series. It decides where and when the next conference will be held, and selects the Program Chair(s).
- Xindong Wu (Chair), University of Vermont, USA
- Max Bramer, University of Portsmouth, UK.
- Nick Cercone, Dalhousie University, Canada.
- Ramamohanarao Kotagiri, University of Melbourne, Australia.
- Vipin Kumar, University of Minnesota, USA.
- Katharina Morik, University of Dortmund, Germany.
- Gregory Piatetsky-Shapiro, KDnuggets, USA.
- Benjamin W. Wah, University of Illinois, Urbana-Champaign, USA. 数据挖掘研究院
- Philip S. Yu, IBM T.J. Watson Research Center, USA.
- Ning Zhong, Maebashi Institute of Technology, Japan.
Conference Publications
ICDM proceedings are expected to be published by the IEEE Computer Society Press. A selected number of ICDM accepted papers will be expanded and revised for possible inclusion in the KAIS journal (Knowledge and Information Systems, by Springer-Verlag) each year. This will be mentioned in all calls for papers of the ICDM conference. KAIS will publish the calls for papers of the ICDM conferences once a year to publicize the mutual support for the success of ICDM and KAIS.
