Información de la conferencia
SDM 2020: SIAM International Conference on Data Mining
Día de Entrega:
Fecha de Notificación:
Fecha de Conferencia:
Cincinnati, Ohio, USA
CCF: b   CORE: a   QUALIS: a2   Vistas: 35303   Seguidores: 132   Asistentes: 11

Solicitud de Artículos
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

    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


    Astronomy & Astrophysics
    Automation & Process Control
    Climate / Ecological / Environmental  Science
    Customer Relationship Management
    Data Science
    Drug Discovery
    Genomics & Bioinformatics
    Healthcare Management
    High Energy Physics
    Intelligence Analysis
    Internet of Things
    Intrusion & Fraud detection
    Logistics Management
    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
Última Actualización Por Dou Sun en 2019-09-01
Coeficiente de Aceptación
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Revistas Relacionadas
CCFNombre CompletoFactor de ImpactoEditorISSN
aSIAM Journal on Computing SIAM0097-5397
Statistical Analysis and Data Mining John Wiley & Sons, Ltd1932-1872
aIEEE Transactions on Knowledge and Data Engineering3.857IEEE1041-4347
ACM SIGMIS Database ACM0095-0033
Crisis Communications Springer2194-9794
cIEEE Signal Processing Letters3.268IEEE1070-9908
bSIAM Journal on Imaging Sciences SIAM1936-4954
bData & Knowledge Engineering1.583Elsevier0169-023X
bData Mining and Knowledge Discovery3.16Springer1384-5810
Journal of Digital Learning in Teacher EducationTaylor & Francis2153-2974