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.

Topics

PAKDD 2020 welcomes high-quality, original and previously unpublished submissions in the theory, practice, and applications on all aspects of knowledge discovery and data mining.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:

• Anomaly detection and analytics
• Association analysis
• Classification
• Clustering
• Data pre-processing
• Deep learning theory and applications in KDD
• Explainable machine learning
• Factor and tensor analysis
• Feature extraction and selection
• Fraud and risk analysis
• Human, domain, organizational, and social factors in data mining
• Integration of data warehousing, OLAP, and data mining
• Interactive and online mining
• Mining behavioral data
• Mining dynamic/streaming data
• Mining graph and network data
• Mining heterogeneous/multi-source data
• Mining high dimensional data
• Mining imbalanced data
• Mining multi-media data
• Mining scientific data
• Mining sequential data
• Mining social networks
• Mining spatial and temporal data
• Mining uncertain data
• Mining unstructured and semi-structured data
• Novel models and algorithms
• Opinion mining and sentiment analysis
• Parallel, distributed, and cloud-based high-performance data mining
• Post-processing including quality assessment and validation
• Privacy preserving data mining
• Recommender systems
• Representation learning and embedding
• Security and intrusion detection
• Statistical methods and graphical models for data mining
• Supervised learning
• Theoretic foundations of KDD
• Ubiquitous knowledge discovery and agent-based data mining
• Unsupervised learning
• Visual data mining
• Applications to healthcare, bioinformatics, computational chemistry, finance, eco-informatics, marketing, gaming, cyber-security, and industry-related problems


Paper Submission

Paper submission must be in English. 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. 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 initial 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.

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 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.

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.

Double-Blind Review

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 own 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 2020. An exception to this rule applies to manuscripts that were published in arXiv not later than 25 October 2019, i.e., at least a month prior to PAKDD’s submission deadline. These can be submitted to PAKDD provided that the submitted paper’s title and abstract are different from the one appearing on arXiv. Any submission shall not appear in arXiv until the review process has ended.


Formatting template: http://www.springer.de/comp/lncs/authors.html

Submission site: https://cmt3.research.microsoft.com/PAKDD2020

Contact Information

If you have any questions, please feel free to contact us at pakdd2020@gmail.com

Hady Lauw, Raymond Wong, Alexandros Ntoulas
Program Co-Chairs of PAKDD 2020