SIAM International Conference on Data Mining, SDM 会议怎么样？

根据，https://blog.csdn.net/Strive_For_Future/article/details/122133367，

SDM会议在数据挖掘顶会的第二等级（ECCV在该表中也排在第二等），所以可信度还是比较高的，遇到文章还是值得一读。

SDM (2+): 数据挖掘方面仅次于SIGKDD的会议, 目前和ICDM相当. SIAM的底子很厚, 但在CS里面的影响比ACM和IEEE还是要小, SDM眼看着要被ICDM超过了, 但至少目前还是相当的.

以下内容为转载（博客）：

本博文借介绍SIAM International Conference on DATA MINING的机会，把数据挖掘相关信息介绍一下，重点是算法、应用的分类。

SIAM International Conference on DATA MINING（SDM），其中SIAM表示的是Society for Industrial and Applied Mathematics。

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.

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 Image Data
Mining on Emerging Architectures
Text & Web Mining
Optimization Methods
Other Novel Methods
Applications

Astronomy & Astrophysics
High Energy Physics
Recommender Systems
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 Network Analysis
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
可见推荐系统也是数据挖掘的应用。

SDM官网https://archive.siam.org/meetings/sdm18/
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版权声明：本文为CSDN博主「存在computer」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/u014622100/article/details/110121553

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