Proceedings of the 2024 SIAM International Conference on Data Mining (SDM):
Data mining is an important tool in science, engineering, industrial processes, healthcare, business, and medicine. The datasets in these fields are large, complex, and often noisy. Extracting knowledge requires the use of sophisticated, high performance and principled analysis techniques and algori...
Gespeichert in:
| Weitere Verfasser: | , , , , |
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| Format: | Elektronisch Tagungsbericht E-Book |
| Sprache: | English |
| Veröffentlicht: |
Philadelphia, [Pennsylvania]
Society for Industrial and Applied Mathematics, SIAM
[2024]
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| Schlagworte: | |
| Online-Zugang: | DE-29 Volltext |
| Zusammenfassung: | Data mining is an important tool in science, engineering, industrial processes, healthcare, business, and medicine. The datasets in these fields are large, complex, and often noisy. Extracting knowledge requires the use of sophisticated, high performance and principled analysis techniques and algorithms, based on sound theoretical and statistical foundations. These techniques in turn require implementations that are carefully tuned for performance; powerful visualization technologies; interface systems that are usable by scientists, engineers, and physicians as well as researchers; and infrastructures that support them. This 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 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 are 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. |
| Beschreibung: | 1 Online-Ressource (xii, 904 Seiten) |
| ISBN: | 9781611978032 |
| DOI: | 10.1137/1.9781611978032 |
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| spelling | SIAM International Conference on Data Mining 2024 Houston, [Texas] isb Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) editor(s): Shashi Shekhar, Vagelis Papalexakis, Jing Gao, Zhe Jiang, and Matteo Riondato SDM'24 SDM 2024 Philadelphia, [Pennsylvania] Society for Industrial and Applied Mathematics, SIAM [2024] © 2024 1 Online-Ressource (xii, 904 Seiten) txt rdacontent c rdamedia cr rdacarrier Data mining is an important tool in science, engineering, industrial processes, healthcare, business, and medicine. The datasets in these fields are large, complex, and often noisy. Extracting knowledge requires the use of sophisticated, high performance and principled analysis techniques and algorithms, based on sound theoretical and statistical foundations. These techniques in turn require implementations that are carefully tuned for performance; powerful visualization technologies; interface systems that are usable by scientists, engineers, and physicians as well as researchers; and infrastructures that support them. This 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 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 are 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. (DE-588)1071861417 Konferenzschrift gnd-content Shekhar, Shashi 1963- (DE-588)170860566 edt Papalexakis, Vagelis edt Gao, Jing edt Jiang, Zhe edt Riondato, Matteo (DE-588)1257161466 edt https://doi.org/10.1137/1.9781611978032 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) |
| subject_GND | (DE-588)1071861417 |
| title | Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) |
| title_alt | SDM'24 SDM 2024 |
| title_auth | Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) |
| title_exact_search | Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) |
| title_full | Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) editor(s): Shashi Shekhar, Vagelis Papalexakis, Jing Gao, Zhe Jiang, and Matteo Riondato |
| title_fullStr | Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) editor(s): Shashi Shekhar, Vagelis Papalexakis, Jing Gao, Zhe Jiang, and Matteo Riondato |
| title_full_unstemmed | Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) editor(s): Shashi Shekhar, Vagelis Papalexakis, Jing Gao, Zhe Jiang, and Matteo Riondato |
| title_short | Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) |
| title_sort | proceedings of the 2024 siam international conference on data mining sdm |
| topic_facet | Konferenzschrift |
| url | https://doi.org/10.1137/1.9781611978032 |
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