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.
Topics of Interest:
Topics of interest include, but are not limited to:
- Foundations, algorithms, models, and theory of data mining
- Machine learning and statistical methods for data mining
- Mining text, semi-structured, spatio-temporal, streaming, graph, web, multimedia data
- Data mining systems and platforms, their efficiency, scalability, and privacy
- Data mining in modeling, visualization, personalization, and recommendation
- Applications of data mining in all domains including social, web, bioinformatics, and finance
Submission Guidelines:
Paper submissions should be limited to a maximum of ten (10) pages, in the IEEE 2- column format (templates available at here), including the bibliography and any possible appendices. Submissions longer than 10 pages will be rejected without a review. All submissions will be triple-blind reviewed by the Program Committee on the basis of technical quality, relevance to data mining, originality, significance, and clarity. Author names and affiliations must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity. Accepted papers will be published in the conference proceedings by the IEEE Computer Society Press. All manuscripts are submitted as full papers and are reviewed based on their scientific merit. There is no separate abstract submission step. There are no separate industrial, application, short paper or poster tracks. Manuscripts must be submitted electronically in online submission system (click here). We do not accept email submissions.
Repeatability:
Every effort should be made to enable the code and the datasets (or their closest publicly available equivalents) and all relevant parameter specifications available to reviewers and readers so that the results can be independently replicated. While we cannot expect *every* dataset used to be made public, it is in the best interest of the authors to make as many as possible available (in the worst case, a sampled or an anonymized version). To retain anonymity of submissions, any such additional material should be anonymized as well, and be published on an anonymous website (such as DropBox). The repeatability factor will play an important role in the reviewer's assessment of the submission.
Best Paper Awards:
Awards will be conferred at the conference on the authors of the best research paper, the best application paper, and the best student paper. A selected number of accepted papers will be invited for possible inclusion, in an expanded and revised form, in the Knowledge and Information Systems journal published by Springer-Verlag.
Attendance:
ICDM is forum for presenting and discussing current research in data mining. Therefore, at least one author of an accepted paper must complete a regular conference registration and present the paper in the conference, in order for the paper to be included in the proceedings and program.
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Charu Aggarwal IBM Research |
Zhi-Hua Zhou Nanjing University, China |
(please email icdm15pc@gmail.com if you want to contact the PC chairs; emails to personnel accounts will not get response)