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 […]
Aims and Scope
The IEEE International Conference on Data Mining (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 and practical development experiences. The conference covers all aspects of data mining, including algorithms, software, systems, and applications. ICDM draws researchers, application developers, and practitioners from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases, 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 advance the state-of-the-art in data mining.
Paper submissions should be limited to a maximum of ten (10) pages, in the IEEE 2-column format (link), including the bibliography and any possible appendices. Submissions longer than 10 pages will be rejected without review. All submissions will be triple-blind reviewed by the Program Committee on the basis of technical quality, relevance to scope of the conference, originality, significance, and clarity. The following sections give further information for authors.
Triple blind submission guidelines
Since 2011, ICDM has imposed a triple blind submission and review policy for all submissions. Authors must hence not use identifying information in the text of the paper and bibliographies must be referenced to preserve anonymity. Any papers available on the Web (including Arxiv) no longer qualify for ICDM submissions, as their author information is already public.
What is triple blind reviewing?
The traditional blind paper submission hides the referee name from the authors. The triple-blind paper submission and review, in addition, also hides the authors’ names from the referees, and the referees’ names during discussion. The names of authors and referees remain known only to the PC co-chairs, and the authors names are disclosed only after the ranking and acceptance of submissions are finalized. Although there is much debate on the merits and perceived benefits of triple blind reviewing, these are not discussed here. Our main purpose is to implement this policy in ICDM toward understanding the influence of the authors’ identity, whether conscious or unconscious, on the reviewer’s attitude toward a submission. Hence it is imperative that all authors of ICDM submissions work on concealing their identity in the content of the paper. It does not suffice to simply remove the authors’ names from the first page.
How to prepare your submissions
The authors shall omit their names from the submission. For formatting templates with author and institution information, simply replace all these information in the template by “Anonymous”.
- In the submission, the authors’ should refer to their own prior work like the prior work of any other author, and include all relevant citations. This can be done either by referring to their prior work in the third person or referencing papers generically. For example, if your name is Smith and you have worked on clustering, instead of saying “We extend our earlier work on distance-based clustering (Smith 2005),” you might say “We extend Smith’s (Smith 2005) earlier work on distance-based clustering.”
- The authors shall exclude citations to their own work which is not fundamental to understanding the paper, including prior versions (e.g., technical reports, unpublished internal documents) of the submitted paper. They should reference only necessary work using point (2). Hence, do not write: “In our previous work ” as it reveals that citation 3 is written by the current authors.
- The authors shall remove mention of funding sources, personal acknowledgments, and other such auxiliary information that could be related to their identities. These can be reinstituted in the camera-ready copy once the paper is accepted for publication.
- The authors shall make statements on well-known or unique systems that identify an author, as vague in respect to identifying the authors as possible.
- The submitted files shall be named with care to ensure that authors’ anonymity is not compromised by the file name. For example, do not name your submission “Smith.pdf”, instead give it a name that is descriptive of the title of your paper, such as “ANewApproachtoClustering.pdf” (or a shorter version of the same).
- Accepted papers will be published in the conference proceedings by the IEEE Computer SocietyPress.
All manuscripts are submitted as full papers and are reviewed based on their scientific merit. The reviewing process is confidential. 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. We do not accept email submissions.
Best Paper Awards
Awards will be conferred at the conference to the authors of the best paper and the best student paper. A selected number of best papers will be invited for possible inclusion, in an expanded and revised form, in the Knowledge and Information Systems journal published by Springer.
ICDM is a premier forum for presenting and discussing current research in data mining. Therefore, at least one author of each accepted paper must complete the conference registration and present the paper at the conference, in order for the paper to be included in the proceedings and conference program.
Topics of interest include, but are not limited to:
- Foundations, algorithms, models and theory of data mining, including big data mining.
- Machine learning and statistical methods for data mining.
- Mining from heterogeneous data sources, including text, semi-structured, spatio-temporal, streaming, graph, web, and multimedia data.
- Data mining systems and platforms, and their efficiency, scalability, security and privacy.
- Data mining for modeling, visualization, personalization, and recommendation.
- Data mining for cyber-physical systems and complex, time-evolving networks.
- Applications of data mining in social sciences, physical sciences, engineering, life sciences, web, marketing, finance, precision medicine, health informatics, and other domains.
We particularly encourage submissions in emerging topics of high importance such as data quality, time-evolving networks, big data mining and analytics, cyber-physical systems, and heterogeneous data integration and mining.
- Feida Zhu, Singapore Management University
- Jeffrey Yu, The Chinese University of Hong Kong
- Dacheng Tao, The University of Sydney
- Bhavani Thuraisingham, The University of Texas at Dallas
Steering Committee Chair
- Xindong Wu, University of Louisiana at Lafayette
- Leman Akoglu, Carnegie Mellon University
- Yong Ge, University of Arizona
- Hanghang Tong, Arizona State University
- Jessie Zhenghui Li, Penn State University
- William Wang, University of California at Santa Barbara
- Peng Cui, Tsinghua University
- Yanfang (Fanny) Ye, West Virginia University
Local Arrangement Chair
- Bing Tian Dai, Singapore Management University
- Wei Ding, University of Massachusetts – Boston
- Siyuan Liu, Penn State University
- Tim Weninger, University of Notre Dame
- To be announced
Data Challenge Chairs
- Xiang Ren, University of Southern California
- Yuchen Li, Singapore Management University
- Minghui Qiu, Algorithm Expert, Alibaba Group
- Kyong Jin Shim, Singapore Management University
- Aixin Sun, Nanyang Technological University
- Danai Koutra, University of Michigan
- Bo Jin, Dalian University of Technology
PhD Forum Chairs
- Ranga Raju VAatsavai, North Carolina State University
- Yi Yu, National Institute of Informatics (NII)
- David Lo, Singapore Management University