About
Data mining is the computational process for discovering valuable knowledge from data. 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, and application developers from different disciplines.
The SDM conference, supported byU.S. National Science Foundation, 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 and others 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.
Themes
Methods and Algorithms
- Classification
- Clustering
- Frequent Pattern Mining
- Probabilistic & Statistical Methods
- Graphical Models
- Spatial & Temporal Mining
- Data Stream Mining
- Anomaly & Outlier Detection
- Feature Extraction, Selection and Dimension Reduction
- Mining with Constraints
- Data Cleaning & Preprocessing
- Computational Learning Theory
- Multi-Task Learning
- Online Algorithms
- Big Data, Scalable & High-Performance Computing Techniques
- Mining with Data Clouds
- Mining Graphs
- Mining Semi Structured Data
- Mining Complex Datasets
- Mining on Emerging Architectures
- Text & Web Mining
- Optimization Methods
- Other Novel Methods
Applications
- Astronomy & Astrophysics
- High Energy Physics
- Collaborative Filtering
- Climate / Ecological / Environmental Science
- Risk Management
- Supply Chain Management
- Customer Relationship Management
- Finance
- Genomics & Bioinformatics
- Drug Discovery
- Healthcare Management
- Automation & Process Control
- Logistics Management
- Intrusion & Fraud detection
- Bio-surveillance
- Sensor Network Applications
- Social and Information Network Analyses
- Educational Data Mining
- Intelligence Analysis
- Other Novel Applications & Case Studies
Human Factors and Social Issues
- Ethics of Data Mining
- Intellectual Ownership
- Privacy Models
- Privacy Preserving Data Mining & Data Publishing
- Risk Analysis
- User Interfaces
- Interestingness & Relevance
- Data & Result Visualization
- Other Human Factors and Social Issues
About SIAM
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 towards this commitment.
Registration Information
Registration fees will be posted here in late October 2016. Online registration will be available in late January 2017.
The pre-registration deadline is March 30, 2017.
For more information click "Further official information" below.
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