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HTTP/2 200 server: nginx date: Tue, 30 Dec 2025 04:18:53 GMT content-type: text/html last-modified: Wed, 05 Oct 2022 23:44:09 GMT vary: Accept-Encoding etag: W/"633e16c9-3446" strict-transport-security: max-age=31536000 content-encoding: gzip PAKDD 2022

Welcome to PAKDD2022

Welcome to the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2022), which will be held in Chengdu, China on May 16-19, 2022. The 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 2022 is in the list of CCF recommended Conferences (C series) .

Call for Paper Flyer

Research Topics

PAKDD2022 welcomes high-quality, original, and previously unpublished submissions in the theories, technologies and applications on all aspects of knowledge discovery and data mining. Topics of relevance for the conference include, but not limited to, the following:

Data Science
Methods for analyzing scientific and business data, social networks, time series; mining sequences, streams, text, web, graphs, rules, patterns, logs data, IoT data, spatio-temporal data, biological data; recommender systems, computational advertising, multimedia, finance, bioinformatics.

Big Data Technologies
Large-scale systems for text and graph analysis, sampling, parallel and distributed data mining (cloud, map-reduce, federated learning), novel algorithmic, and statistical techniques for big data.

Foundations
Models and algorithms, asymptotic analysis; model selection, dimensionality reduction, relational/structured learning, matrix and tensor methods, probabilistic and statistical methods; deep learning, meta-learning, reinforcement learning; classification, clustering, regression, semi-supervised and unsupervised learning; personalization, security and privacy, visualization; fairness, interpretability, and robustness.

Publication

Springer will publish the proceedings of the conference as a volume of the LNAI, and selected excellent papers will be invited for publications in special issue International Journal of Data Science and Analytics.

Please visit the below webistes for PAKDD2022 Proceedings, Part I, Part II, Part III.

https://link.springer.com/book/10.1007/978-3-031-05933-9

https://link.springer.com/book/10.1007/978-3-031-05936-0

https://link.springer.com/book/10.1007/978-3-031-05981-0