CFP

Sponsored by the IEEE Computer Society October 28 - 31, 2007 Embassy Suites Hotel, Omaha, NE, USA

http://www.ist.unomaha.edu/icdm2007/

(Papers Due: Friday June 1st, 2007) (Submission of title/abstract a week in advance is encouraged; revision of submission is possible until the deadline.)

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 this year, the ICDM data mining contest.

Topics of Interest
Topics related to the design, analysis, and implementation of data mining applications are of interest. These include, but are not limited to:

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 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

• 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

• 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

http://www.ist.unomaha.edu/icdm2007/

Conference Co-Chairs:

• Yong Shi, University of Nebraska at Omaha (USA)
• Christopher W. Clifton, Purdue University (USA)

• Naren Ramakrishnan, Virginia Tech (USA)
• Osmar Zaiane, University of Alberta (Canada)