Conference Information
PAKDD 2022: Pacific-Asia Conference on Knowledge Discovery and Data Mining
http://pakdd.net/
Submission Date:
2021-10-31
Notification Date:
2021-12-17
Conference Date:
2022-05-16
Location:
Chengdu, China
Years:
26
CCF: c   CORE: a   Viewed: 91609   Tracked: 269   Attend: 61

Call For Papers
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

PAKDD2022 welcomes high-quality, original, and previously unpublished submissions in the theories, technologies and applications on all aspects of knowledge discovery and data mining.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
Last updated by Dou Sun in 2021-10-24
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