The document "PAKDD 2018" was published on "2022-10-19T14:18:35" on the website "ConfIDent" under the URL https://confident-conference.org/index.php/Event:PAKDD 2018.
The document "PAKDD 2018" describes an event in the sense of a conference.
The document "PAKDD 2018" contains information about the event "PAKDD 2018" with start date "2018/06/03" and end date "2018/06/06".
The event "PAKDD 2018" is part of the event series identified by [[Event Series:PAKDD]]
Deadlines
Metrics
Submitted Papers
592
Accepted Papers
164
Venue
Melbourne, Victoria, Australia
Warning: Venue is missing. The map might not show the exact location.
Topics
As a premier international conference on knowledge discovery and data mining, PAKDD’18 welcomes all submissions on all aspects of knowledge discovery, data mining and machine learning. Suggestive 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
