Difference between revisions of "PAKDD 2020"
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|has Proceedings Link=https://link.springer.com/book/10.1007%2F978-3-030-47436-2 | |has Proceedings Link=https://link.springer.com/book/10.1007%2F978-3-030-47436-2 | ||
|Acronym=PAKDD 2020 | |Acronym=PAKDD 2020 | ||
| − | |End date=2020 | + | |End date=2020-05-14 |
|Series =PAKDD | |Series =PAKDD | ||
|Type =Conference | |Type =Conference | ||
| Line 10: | Line 10: | ||
|State =SG/SG | |State =SG/SG | ||
|City =SG/SG/Singapore | |City =SG/SG/Singapore | ||
| + | |Year =2020 | ||
|Homepage=https://pakdd2020.org/ | |Homepage=https://pakdd2020.org/ | ||
| − | |Start date=2020 | + | |Start date=2020-05-11 |
|Title =24th Pacific-Asia Conference on Knowledge Discovery and Data Mining | |Title =24th Pacific-Asia Conference on Knowledge Discovery and Data Mining | ||
|Accepted papers=135 | |Accepted papers=135 | ||
| − | |Submitted papers=628}} | + | |Submitted papers=628 |
| + | }} | ||
Due to the unexpected COVID-19 epidemic, we made all the conference | Due to the unexpected COVID-19 epidemic, we made all the conference | ||
sessions accessible online to participants around the world. | sessions accessible online to participants around the world. | ||
Latest revision as of 02:48, 6 December 2021
Event Rating
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List of all ratings can be found at PAKDD 2020/rating
| 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 | |
| Location: | SG/SG/Singapore, SG/SG, SG |
| Papers: | Submitted 628 / Accepted 135 (21.5 %) |
| Committees | |
| 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.
Topics
- 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