Data Mining and Revenue Management Methodologies In College Admissions.
The article offers an approach to the college admission process by partitioning. The data mining and revenue management phases of partitioning which can be applied to find solutions for both applicants and colleges are explored. Student applications can be processed dynamically and the college can s...
| Publicado en: | Communications of the ACM Vol. 53; no. 4; pp. 128 - 134 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Association for Computing Machinery
Apr2010
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | The article offers an approach to the college admission process by partitioning. The data mining and revenue management phases of partitioning which can be applied to find solutions for both applicants and colleges are explored. Student applications can be processed dynamically and the college can select the best candidate at each point throughout admissions. The data-mining techniques of supervised data mining such as decision tree and neural networks, which can be used to classify students based on their predicted freshman grade point average are noted. The dynamic model that was used with Markovian time periods to generate a bid price table is discussed. |
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