SIAM Conference



SIAM International Conference on Data Mining (SDM20)

May 7 - 9, 2020

Hilton Cincinnati Netherland Plaza | Cincinnati, Ohio, U.S.

#SIAMSDM20    

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About the Conference


About the Conference

SDM20_logo_data_mining_600px.png

Sponsored by the SIAM Activity Group on Data Mining and Analytics.
This conference is held in cooperation with the American Statistical Association.

This meeting is co-located with the SIAM Conference on Mathematics of Data Science (MDS20), May 5-7, 2020.

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


Included Themes

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
  • 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

  • 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 & 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

Conference Committees


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Thank you to our sponsors

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Thank you to our sponsors

Statement on Inclusiveness

STATEMENT ON INCLUSIVENESS

As a professional society, SIAM is committed to providing an inclusive climate that encourages the open expression and exchange of ideas, that is free from all forms of discrimination, harassment, and retaliation, and that is welcoming and comfortable to all members and to those who participate in its activities. In pursuit of that commitment, SIAM is dedicated to the philosophy of equality of opportunity and treatment for all participants regardless of gender, gender identity or expression, sexual orientation, race, color, national or ethnic origin, religion or religious belief, age, marital status, disabilities, veteran status, field of expertise, or any other reason not related to scientific merit. This philosophy extends from SIAM conferences, to its publications, and to its governing structures and bodies. We expect all members of SIAM and participants in SIAM activities to work toward this commitment.

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