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PAKDD 2021 : The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining

Delhi, India
Event Date: May 11, 2021 - May 14, 2021
Abstract Submission Deadline: Nov 23, 2020
Submission Deadline: Nov 30, 2020
Notification of Acceptance: Feb 01, 2021
Camera Ready Version Due : Feb 24, 2021

About

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.

Call For Paper

PAKDD 2021 welcomes high-quality, original, and previously unpublished submissions in the theory, practice, and applications on all aspects of knowledge discovery and data mining.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

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