20th IEEE International Conference on Data Mining

Sorrento, Italy

November 17-20, 2020

temporary web pages





General Chairs

Alfredo Cuzzocrea, University of Calabria, Italy

Carlo Zaniolo, University of California, Los Angeles, USA


Steering Committee Chair

Xindong Wu, Mininglamp Academy of Sciences, China


-Organizing Committee

Main Contact

Alfredo Cuzzocrea, University of Calabria, Italy


Program Chairs

Claudia Plant, University of Vienna, Austria

Haixun Wang, WeWork, New York, USA


Demo Chairs

Jiannong Cao, The Hong Kong Polytechnic University, Hong Kong

Arno Siebes, University of Utrecht, The Netherlands


Workshop Chairs

Giuseppe Di Fatta, University of Reading, UK

Victor Sheng, Texas Tech University, USA


Contest Chairs

Kaibo Xu, Mininglamp Academy of Sciences, China

Xindong Wu, Mininglamp Academy of Sciences, China


Tutorial Chairs

Diane Cook, Washington State University, USA

Min-Ling Zhang, Southeast University, China


Panel Chairs

Ranga Vatsavai, North Carolina State University, USA

Wei Wang, University of New South Wales, Australia


Local Arrangement Chairs

Giuseppe Polese, University of Salerno, Italy

Vincenzo Moscato, University of Naples “Federico II”, Italy


Publicity Chairs

Ting Bai, Beijing University of Posts and Telecommunications, China

Carson Leung, University of Manitoba, Canada

Washio Takashi, Osaka University, Japan


Finance Chair

Joao Gama, University of Porto, Portugal


Sponsor Chairs

Jieping Ye, DiDi, China



-Preliminary Call for Papers (tentative)

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 big data, deep learning, pattern recognition, statistical and machine learning, 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.


Topics of Interest

Topics of interest include, but are not limited to:

·        Foundations, algorithms, models and theory of data mining, including big data mining.

·        Deep 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 modelling, 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 ethical data analytics, automated data analytics, data-driven reasoning, interpretable modeling, modeling with evolving environment, cyber-physical systems, multi-modality data mining, and heterogeneous data integration and mining.


Submission Guidelines

Paper submissions should be limited to a maximum of ten (10) pages, in the IEEE 2-column format, 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 names from the authors, and the double-blind paper submission also hides the author names from the referees. The triple-blind reviewing further hides the referee names among referees during paper discussions before their acceptance decisions. The names of authors and referees remain known only to the PC Co-chairs, and the author names are disclosed only after the ranking and acceptance of submissions are finalized. It is imperative that all authors of ICDM submissions conceal their identity and affiliation information in their paper submissions. It does not suffice to simply remove the author names and affiliations from the first page, but also in the content of each paper submission.


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. Hence, do not write: “In our previous work [3]” 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 author 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).

Algorithms and resources used in a paper should be described as completely as possible to allow reproducibility. This includes experimental methodology, empirical evaluations, and results. Authors are strongly encouraged to make their code and data publicly available whenever possible. In addition, authors are strongly encouraged to also report, whenever possible, results for their methods on publicly available datasets.

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. The reviewing process is confidential. There is no separate abstract submission step. There are no separate industrial, application, short paper or poster tracks during submission. Manuscripts must be submitted electronically in the 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.


Important Dates

(All deadlines are at 11:59PM Pacific Daylight Time)

·        Workshop proposals: March 2, 2020

·        Workshop notification: March 30, 2020

·        Full paper submissions: June 2, 2020

·        Contest requirement specification and sample data available: June 12, 2020 (Contest)

·        Team registration begins. Data sets are available: June 26, 2020 (Contest)

·        Demo and tutorial proposals: July 10, 2020

·        Final submission deadline: 23:59, July 31, 2020, Pacific Time. (Contest)

·        Workshop paper submissions: August 24, 2020

·        Conference paper, tutorial, demo notification: August 20, 2020

·        Notifications to shortlisted teams for web applications: 23:59, August 10, 2020, Pacific Time. (Contest)

·        Finalist notifications: August 28, 2020 (Contest)

·        Workshop paper notification: September 17, 2020

·        Camera-ready deadline and copyright forms: September 24, 2020

·        Conference dates: November 17-20, 2020

·        Prize presentations at ICDM 2020 and ICBK 2020: November 19, 2020 (Contest)


More Information

More information about ICDM 2020 are available as follows:

·        Website (preliminary version): http://www.icdm2020.unical.it/

·        Email: ICDM2020Chairs@gmail.com

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