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会议名称(中文)：  2012年IEEE国际数据挖掘会议  
会议名称(英文)：  IEEE International Conference on Data Mining  
所属学科：  计算数学与科学工程计算,计算机科学理论,计算机软件,计算机应用技术  
开始日期：  2012-12-10  
结束日期：  2012-12-13  
所在国家：  比利时  
所在城市：      比利时  
具体地点：  Brussels, Belgium  
主办单位：  IEEE Computer Society  
E-MAIL：  icdm@cs.uvm.edu  
通讯地址：   
邮政编码：   
会议注册费：   
会议网站：  http://icdm2012.ua.ac.be/
会议背景介绍：  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.

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.

征文范围及要求：  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