Data mining is the computational process for discovering valuable knowledge from data – the core of modern Data Science. It has enormous applications 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. 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.

SDM has established itself as a leading conference in the field of data mining and provides a venue for researchers who are addressing these problems to present their work in a peer-reviewed forum. SDM emphasizes principled methods with solid mathematical foundation, is known for its high-quality and high-impact technical papers, and offers a strong workshop and tutorial program (which are included in the conference registration). The proceedings of the conference are published in archival form, and are also made available on the SIAM web site.


Included Themes

Methods and Algorithms:
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Anomaly & Outlier Detection
Big Data & Large-Scale Systems
Classification & Semi-Supervised Learning
Clustering & Unsupervised Learning
Data Cleaning & Integration
Deep Learning & Representation Learning
Frequent 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:
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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:
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Ethics of Data Mining
Intellectual Ownership
Interestingness & 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