Difference between revisions of "PAKDD 2019"
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|has Proceedings Link=https://link.springer.com/book/10.1007%2F978-3-030-16142-2 | |has Proceedings Link=https://link.springer.com/book/10.1007%2F978-3-030-16142-2 | ||
|Acronym=PAKDD 2019 | |Acronym=PAKDD 2019 | ||
| − | |End date=2019 | + | |End date=2019-04-17 |
|Series =PAKDD | |Series =PAKDD | ||
|Type =Conference | |Type =Conference | ||
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|State =CN/MO | |State =CN/MO | ||
|City =CN/MO/Macau | |City =CN/MO/Macau | ||
| + | |Year =2019 | ||
|Homepage=https://pakdd2019.medmeeting.org/Content/100312 | |Homepage=https://pakdd2019.medmeeting.org/Content/100312 | ||
| − | |Start date=2019 | + | |Start date=2019-04-14 |
|Title =23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining | |Title =23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining | ||
|Accepted papers=135 | |Accepted papers=135 | ||
| − | |Submitted papers=628}} | + | |Submitted papers=628 |
| + | }} | ||
Topics | Topics | ||
Latest revision as of 03:37, 6 December 2021
Event Rating
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List of all ratings can be found at PAKDD 2019/rating
| PAKDD 2019 | |
|---|---|
23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining
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| Event in series | PAKDD |
| Dates | 2019-04-14 (iCal) - 2019-04-17 |
| Homepage: | https://pakdd2019.medmeeting.org/Content/100312 |
| Location | |
| Location: | CN/MO/Macau, CN/MO, CN |
| Papers: | Submitted 628 / Accepted 135 (21.5 %) |
| Committees | |
| General chairs: | Qiang Yang, Zhi-Hua Zhou |
| PC chairs: | Zhiguo Gong, Min-Ling Zhang |
| Workshop chairs: | Hady W. Lauw, Leong Hou U |
| Table of Contents | |
Topics
PAKDD 2019 welcomes high-quality, original and previously unpublished submissions in the theory, practice, and applications on all aspects of knowledge discovery and data mining.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: ● Theoretic foundations of KDD ● Deep learning theory and applications in KDD ● Novel models and algorithms ● Statistical methods and graphical models for data mining ● Anomaly detection and analytics ● Association analysis ● Clustering ● Classification ● Data pre-processing ● Feature extraction and selection ● Post-processing including quality assessment and validation ● Mining heterogeneous/multi-source data ● Mining sequential data ● Mining spatial and temporal data ● Mining unstructured and semi-structured data ● Mining graph and network data ● Mining social networks ● Mining high dimensional data ● Mining uncertain data ● Mining imbalanced data ● Mining dynamic/streaming data ● Mining behavioral data ● Mining multi-media data ● Mining scientific data ● Privacy preserving data mining ● Fraud and risk analysis ● Security and intrusion detection ● Visual data mining ● Interactive and online mining ● Ubiquitous knowledge discovery and agent-based data mining ● Integration of data warehousing, OLAP, and data mining ● Parallel, distributed, and cloud-based high-performance data mining ● Opinion mining and sentiment analysis ● Human, domain, organizational, and social factors in data mining ● Applications to healthcare, bioinformatics, computational chemistry, finance, eco-informatics, marketing, gaming, cyber-security, and industry-related problems