Difference between revisions of "PAKDD 2020"
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|City=Singapore | |City=Singapore | ||
|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 | ||
| − | sessions accessible online to participants around the world, | + | 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 | ||
Latest revision as of 19:06, 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 | |
| Location: | Singapore, Republic of Singapore |
Loading map... | |
| 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
Facts about "PAKDD 2020"
| Acceptance rate | 21.5 + |
| Accepted papers | 135 + |
| Acronym | PAKDD 2020 + |
| End date | May 14, 2020 + |
| Event in series | PAKDD + |
| Event type | Conference + |
| Has coordinates | 1° 17' 24", 103° 51' 7"Latitude: 1.2899166666667 Longitude: 103.85190833333 + |
| Has general chair | Ee-Peng Lim + and See-Kiong Ng + |
| Has location city | Singapore + |
| Has location country | Category:Republic of Singapore + |
| Has program chair | Hady Lauw +, Raymond Wong + and Alexandros Ntoulas + |
| Homepage | https://pakdd2020.org/ + |
| IsA | Event + |
| Start date | May 11, 2020 + |
| Submitted papers | 628 + |
| Title | 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining + |
