Difference between revisions of "PAKDD 2020"

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|Country=Republic of Singapore
|Country=Republic of Singapore
|has general chair=Ee-Peng Lim, See-Kiong Ng
|has program chair=Hady Lauw, Raymond Wong, Alexandros Ntoulas
|Submitted papers=628
|Accepted papers=135
|has Proceedings Link=https://link.springer.com/book/10.1007%2F978-3-030-47436-2
Due to the unexpected COVID-19 epidemic, we made all the conference
Due to the unexpected COVID-19 epidemic, we made all the conference

Latest revision as of 20:06, 20 May 2020

PAKDD 2020
24th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Event in series PAKDD
Dates 2020/05/11 (iCal) - 2020/05/14
Homepage: https://pakdd2020.org/
Location: Singapore, Republic of Singapore
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Papers: Submitted 628 / Accepted 135 (21.5 %)
General chairs: Ee-Peng Lim, See-Kiong Ng
PC chairs: Hady Lauw, Raymond Wong, Alexandros Ntoulas
Table of Contents

Due to the unexpected COVID-19 epidemic, we made all the conference sessions accessible online to participants around the world.


  • Anomaly detection and analytics
  • Association analysis
  • Classification
  • Clustering
  • Data pre-processing
  • Deep learning theory and applications in KDD
  • Explainable machine learning
  • Factor and tensor analysis
  • Feature extraction and selection
  • Fraud and risk analysis
  • Human, domain, organizational, and social factors in data mining
  • Integration of data warehousing, OLAP, and data mining
  • Interactive and online mining
  • Mining behavioral data
  • Mining dynamic/streaming data
  • Mining graph and network data
  • Mining heterogeneous/multi-source data
  • Mining high dimensional data
  • Mining imbalanced data
  • Mining multi-media data
  • Mining scientific data
  • Mining sequential data
  • Mining social networks
  • Mining spatial and temporal data
  • Mining uncertain data
  • Mining unstructured and semi-structured data
  • Novel models and algorithms
  • Opinion mining and sentiment analysis
  • Parallel, distributed, and cloud-based high-performance data mining
  • Post-processing including quality assessment and validation
  • Privacy preserving data mining
  • Recommender systems
  • Representation learning and embedding
  • Security and intrusion detection
  • Statistical methods and graphical models for data mining
  • Supervised learning
  • Theoretic foundations of KDD
  • Ubiquitous knowledge discovery and agent-based data mining
  • Unsupervised learning
  • Visual data mining
  • Applications to healthcare, bioinformatics, computational chemistry, finance, eco-informatics, marketing, gaming, cyber-security, and industry-related problems