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(领域顶尖会议)数据挖掘领域顶级国际会议“International Conference on Data Mining(ICDM)

时间:2010-09-19 13:07:11  来源:  作者:

http://www.cs.uvm.edu/~icdm/  2009

 

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 features workshops, tutorials, panels and, since 2007, the ICDM data mining contest.

ICDM is held annually, in different regions of the world. The map below shows the locations and conference dates for ICDM 2001 to 2010.

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:

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

David J. Hand, Imperial College, London, UK

Ramamohanarao Kotagiri, University of Melbourne, Australia

Vipin Kumar, University of Minnesota, USA

Heikki Mannila, University of Helsinki, Finland

Gregory Piatetsky-Shapiro, KDnuggets, USA

Shusaku Tsumoto, Shimane University, Japan

Benjamin W. Wah, University of Illinois, Urbana-Champaign, USA

Philip S. Yu, University of Illinois at Chicago, USA

Osmar R. Zaiane, University of Alberta, Canada

Conference Publications
ICDM proceedings are 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 without any charges, by the conference organizers' request, to publicize the mutual support for the success of ICDM and KAIS.

数据挖掘是计算机科学近年来最活跃的领域之一,由于发展迅速,技术更新很快,本领域国际会议被重视的程度甚至超过期刊。由全美计算机协会(ACM) 主办的SIGKDD和国际电子电器工程师协会主办的ICDM是本领域最顶级的两个会议,历年来论文录取率仅在10%左右(中国大陆被录取的文章每年只有几篇),在相关领域有深远的影响。本年度ICDM收到797篇稿件,仅录用了72篇长文(录取率为9.0%)和83篇短文(录取率为10.4%)。

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