Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2021
Event submitted on Monday, October 12th 2020, approved by Henry Dalzel ✓
This event has been tagged as follows:
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.
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.
PAKDD 2021 welcomes high-quality, original, and previously unpublished submissions in the theory, practice, and applications on all aspects of knowledge discovery and data mining.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:
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.
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.
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