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ICDM 2009 : The 2009 IEEE International Conference on Data Mining

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Conference Series : International Conference on Data Mining
 
Link: http://www.cs.umbc.edu/ICDM09/
 
When Dec 6, 2009 - Dec 9, 2009
Where Miami, FLorida ,USA
Submission Deadline Jun 26, 2009
Notification Due Sep 15, 2009
Final Version Due Oct 7, 2009
Categories    data mining
 

Call For Papers

The 2009 IEEE International Conference on Data Mining (ICDM 2009)

December 6 – 9, 2009, Miami, Florida, USA


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.

Paper Submissions

High quality papers in all data mining areas are solicited. Original papers exploring new directions will receive especially careful consideration. Papers that have already been accepted or are currently under review for other conferences or journals will not be considered for ICDM 09.

Paper submissions should be limited to a maximum of 10 pages in the IEEE 2-column format, the same as the camera-ready format (see the IEEE Computer Society Press Proceedings Author Guidelines). All papers will be reviewed by the Program Committee on the basis of technical quality, relevance to data mining, originality, significance, and clarity. A double blind review process will be adopted. Authors should avoid using identifying information in the text of the paper. A Submission Form to submit your work will be announced on the ICDM 09 website.

Accepted papers will be published in the conference proceedings by the IEEE Computer Society Press and accorded oral presentation times in the main conference. Submissions accepted as regular papers will be allocated 10 pages in the proceedings. Submissions accepted as short papers will be allocated 6 pages in the proceedings and will have a shorter presentation time at the conference than regular papers.

A selected number of IEEE ICDM 09 accepted papers will be invited for possible inclusion, in expanded and revised form, in the Knowledge and Information Systems journal published by Springer-Verlag.


ICDM Best Paper Awards

IEEE ICDM Best Paper Awards will be conferred at the conference on the authors of (1) the best research paper and (2) the best application paper. Strong, foundational results will be considered for the best research paper award and application-oriented submissions will be considered for the best application paper award.

Workshops and Tutorials

ICDM ’09 will host short and long tutorials as well as workshops that focus on new research directions and initiatives. All accepted workshop papers will be included in a separate workshop proceedings published by the IEEE Computer Society Press.

ICDM Data Mining Contest

ICDM ’09 will host a data mining contest to challenge researchers and practitioners with a real practical data mining problem. For further details on proposals and expression of interest, please see the Call for Data Mining Context Proposals.

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

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