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The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is one of the longest established and leading international conferences in the areas of data mining and knowledge discovery. It provides an international forum for researchers and industry practitioners to share their new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications.
PAKDD 2021 welcomes high-quality, original, and previously unpublished submissions in the theory, practice, and applications on all aspects of knowledge discovery and data mining. Topics of relevance for the conference include, but not limited to, the following:
Paper submission must be in English. All papers will be double-blind reviewed by the Program Committee based on technical quality, relevance to data mining, originality, significance, and clarity. All paper submissions will be handled electronically. Papers that do not comply with the Submission Policy will be rejected without review.
Each submitted paper should include an abstract up to 200 words and be no longer than 12 single-spaced pages with 10pt font size (including references, appendices, etc.). Authors are strongly encouraged to use Springer LNCS/LNAI manuscript submission guidelines for their submissions. All papers must be submitted electronically through the paper submission system in PDF format only. If required supplementary material may be submitted as a separate PDF file, but reviewers are not obligated to consider this, and your manuscript should, therefore, stand on its own merits without any supplementary material. Supplementary material will not be published in the proceedings.
We require that any submission to PAKDD must not be already published or under review at another archival conference or journal. Papers on arXiv do not violate this rule as long as the submitted paper does not cite them. Submitting a paper to the conference means that if the paper was accepted, at least one author will complete the regular registration and attend the conference to present the paper. For no-show authors, their papers will not be included in the proceedings. Before submitting your paper, please carefully read and agree with the PAKDD Paper Submission Policy and No-Show Policy: https://pakdd.org/policies/.
The conference will confer several awards, including Best Paper Award, Best Student Paper Award, and Best Application Paper Award from the submissions.
Those papers predominately dealing with computer vision core problems are less likely to be relevant to the conference. The reviewers will have less understanding of these problems to evaluate the papers.
Springer will publish the proceedings of the conference as a volume of the LNAI series, and selected excellent papers will be invited for publications in special issues of high-quality journals, including Knowledge and Information Systems (KAIS) and International Journal of Data Science and Analytics.
Paper submission must adhere to the double-blind review policy. Submissions must have all details identifying the author(s) removed from the original manuscript (including the supplementary files, if any), and the author(s) should refer to their prior work in the third person and include all relevant citations.
Because of the double-blind review process, non-anonymous papers that have been issued as technical reports or similar cannot be considered for PAKDD 2021. An exception to this rule applies to manuscripts that were published in arXiv not later than 30th October 2020, i.e., at least a month before PAKDD’s submission deadline.
The author list and order cannot be changed after the paper is submitted.
All the Manuscripts must be prepared and submitted in accordance with the above format. Usage of other formats may lead to disqualification of paper for the conference.
If you have any questions, please feel free to contact us at email@example.com
Kamal Karlapalem, Hong Cheng, Naren Ramakrishnan
Program Co-Chairs of PAKDD 2021