Data mining is the computational process for discovering valuable knowledge from data – the core of Data Science. It has enormous application in numerous fields, including science, engineering, healthcare, business, and medicine. Typical datasets in these fields are large, complex, and often noisy. Extracting knowledge from these datasets requires the use of sophisticated, high-performance, and principled analysis techniques and algorithms, which are based on sound theoretical and statistical foundations. These techniques in turn require implementations on high performance computational infrastructure that are carefully tuned for performance. Powerful visualization technologies along with effective user interfaces are also essential to make data mining tools appealing to researchers, analysts, data scientists and application developers from different disciplines, as well as usable by stakeholders.

The SDM conference provides a venue for researchers who are addressing these problems to present their work in a peer-reviewed forum. It also provides an ideal setting for graduate students to network and get feedback for their work (as part of the doctoral forum) and everyone new to the field to learn about cutting-edge research by hearing outstanding invited speakers and attending presentations and tutorials (included with conference registration). A set of focused workshops is also held on the last day of the conference. The proceedings of the conference are published in archival form and are also made available on the SIAM web site.

TOPICS OF INTEREST
Methods and Algorithms
• Anomaly & Outlier Detection
• Big Data & Large-Scale Systems
• Classification & Semi-Supervised
Learning
• Clustering & Unsupervised Learning
• Data Cleaning & Integration
• Deep Learning & Representation
Learning
• Pattern Mining
• Feature Extraction, Selection and
Dimensionality Reduction
• Mining Data Streams
• Mining Graphs & Complex Data
• Mining on Emerging Architectures
& Data Clouds
• Mining Semi Structured Data
• Mining Spatial & Temporal Data
• Mining Text, Web & Social Media
• Online Algorithms
• Optimization Methods
• Parallel and Distributed Methods
• Probabilistic & Statistical Methods
• Scalable & High-Performance Mining
• Other Novel Methods
Applications
• Astronomy & Astrophysics
• Automation & Process Control
• Climate / Ecological / Environmental
Science
• Customer Relationship Management
• Data Science
• Drug Discovery
• Finance
• Genomics & Bioinformatics
• Healthcare Management
• High Energy Physics
• Intelligence Analysis
• Internet of Things
• Intrusion & Fraud detection
• Logistics Management
• Recommendation
• Risk Management
• Social Network Analysis
• Supply Chain Management
• Other Emerging Applications
Human Factors and Social Issues
• Ethics of Data Mining
• Intellectual Ownership
• Interestingness and Relevance
• Privacy and Fairness Models
• Privacy Preserving Data Mining
• Risk Analysis and Risk Management
• Transparency and Algorithmic Bias
• User Interfaces and Visual Analytics
• Other Human and Social Issues

WORKSHOPS AND TUTORIALS
The conference will feature workshops
and tutorials on several special topics.
Please see the SDM 2021 website for
submission requirements. Examples of
workshops and tutorials are available
through the SDM 2021 website,
go.siam.org/SDM21

IMPORTANT DATES (tentative)
Paper Submission:
October 12, 2021 11:59pm (US Pacific Time)
Workshop Proposals:
October 15, 2021 11:59pm (US Pacific Time)
Tutorial Proposals:
October 15, 2021 11:59pm (US Pacific Time)