Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2021
Event submitted on Monday, October 12th 2020, approved by Content Team ✓
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May 11th, 2021
- May 14th, 2021
India
» Delhi
Event Website
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Our Review
This would be an excellent conference for those living and working in Delhi. PAKDD is one of the longest established and leading international events in the areas of data mining and knowledge discovery. Focuses on new ideas, original research results, and practical development experiences from all KDD related areas.
- Review written by Henry Dalziel on Monday, October 12th 2020.
- If you would like to edit or ammend facts in my review please either send us a message or connect with me via LinkedIn.
Conference Event Summary
The following description was either submitted by the Conference Organizer on Monday, October 12th 2020, or created by us.
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PAKDD 2021 welcomes high-quality, original, and previously unpublished submissions in the theory, practice, and applications on all aspects of knowledge discovery and data mining.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:
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:
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