SIAM International Conference on Data Mining

Aperçu

The SIAM Data Mining (SDM) conference invites submissions of high-quality research papers that present original results on data mining algorithms and their applications. Data mining is a core process within computing and statistics, aimed at discovering valuable knowledge from data. This field has significant applications across various domains including science, engineering, healthcare, business, and medicine. Datasets in these fields are typically large, complex, and noisy, necessitating sophisticated, high-performance analysis techniques grounded in sound theoretical and statistical principles. The SDM conference 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 to network and get feedback for their work (as part of the doctoral forum) and everyone new to the field to learn about cutting-edge research by hearing outstanding invited speakers and attending presentations, tutorials and a number of focused workshops. The proceedings of the conference are published in archival form and are also made available on the SIAM Web site. Submissions that are identical (or substantially similar) to versions that have been previously published, or accepted for publication, or that have been submitted in parallel to this or other conferences or journals, are not allowed and violate our dual submission policy. Papers that have been submitted to archival repositories such as arXiv may be submitted to SDM 2026. All research papers should have a maximum length of eight (8) pages, including all text and figures. References and appendices are unlimited and may not be reviewed. Authors should use U.S. Letter (8.5" x 11") paper size. Papers must be prepared in LaTeX2e, and formatted using SIAM’s double column macro. Review will be triple blind: submissions must be anonymized. Violations of the blind policy will result in rejection without review.

Classement source

Carte