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 |
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Association for Computing Machinery
Apr2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=49090003&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 49090003 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Apr2010 vid: 53 iid: 4 pid: 68 pub: Association for Computing Machinery artinfo: ui: 49090003 10.1145/1721654.1721690 ppf: 128 ppct: 6 formats: tig: atl: Data Mining and Revenue Management Methodologies In College Admissions. aug: au: REBBAPRAGADA, SURYA BASU, AMIT SEMPLE, JOHN affil: Carr P. Collins Chair in MIS, Cox School of Business, Southern Methodist University, Dallas, TX. Software engineer, Verizon Communication in Irving, TX. Charles Wyly Professor of MIS, Cox School of Business, Southern Methodist University, Dallas, TX. su: University & college admission Data mining Revenue management DYNAMO (Computer program language) Markov spectrum College applicants sug: subj: University & college admission Data mining Revenue management DYNAMO (Computer program language) Markov spectrum College applicants ab: 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. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2010 holdings: @attributes: islocal: N |
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