The IEEE International Conference on Data Mining (ICDM) has established itself as the world's premier research conference in data mining. The 2009 edition of ICDM provides a leading 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 the ICDM data mining contest.
Topic 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
Mining in emerging domains
- Stream Data Mining
- Mining moving object data, RFID data, and data from sensor networks
- Ubiquitous knowledge discovery
- Mining multi-agent data
- 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
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
- Integration of data warehousing, OLAP and data mining
- Data mining query languages
- Security and data integrity
Integrated KDD applications, systems, and experiences
- Bioinformatics, computational chemistry, ecoinformatics
- 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
- Sustainable mobility and intelligent transportation systems
- April 13,2009:Workshop proposals
- June 26,2009:Paper submission
- June 26,2009:Panel proposal
- June 26,2009:Tutorial submission
- Sept 4,2009:Notification to authors
- Sept 28,2009:Camera-ready copies
- Dec 6-9,2009: Conference
