The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the areas of knowledge discovery and data mining (KDD). 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.
The submitted paper should adhere to the double-blind review policy. All papers will be double-blind reviewed by the Program Committee on the basis of technical quality, relevance to data mining, originality, significance, and clarity. All paper submissions will be handled electronically. Detailed instructions are provided on the conference home page. Papers that do not comply with the Submission Guidelines 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. Authors are strongly encouraged to use Springer LNCS/LNAI manuscript submission guidelines for their initial submissions. All papers must be submitted electronically through the paper submission system in PDF format only.
The submitted papers must not be previously published anywhere and must not be under consideration by any other conference or journal during the PAKDD review process. Submitting a paper to the conference means that if the paper was accepted, at least one author will attend the conference to present the paper. For no-show authors, their papers will not be included in the proceedings.
The conference will confer several awards including Best Paper Awards, Best Student Paper Awards, and Best Application Paper Awards from the submissions.
The proceedings of the conference will be published by Springer 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.
Before submitting your paper, please carefully read and agree with the PAKDD submission policy and no-show policy: http://pakdd.org/policy.html.