Efficiency of mining algorithms in academic indicators
Data Mining is the process of analyzing data using automated methodologies to find hidden patterns [1]. Data mining processes aim at the use of the dataset generated by a process or business in order to obtain information that supports decision making at executive levels [2] [3] through the automati...
- Autores:
-
amelec, viloria
Hernandez Palma, Hugo Gaspar
Niebles Núñez, William
Gaitán, Mercedes
Pineda Lezama, Bonerge
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/6186
- Acceso en línea:
- https://hdl.handle.net/11323/6186
https://repositorio.cuc.edu.co/
- Palabra clave:
- Data Mining
Mining algorithms
Academic indicators
- Rights
- openAccess
- License
- CC0 1.0 Universal
| Summary: | Data Mining is the process of analyzing data using automated methodologies to find hidden patterns [1]. Data mining processes aim at the use of the dataset generated by a process or business in order to obtain information that supports decision making at executive levels [2] [3] through the automation of the process of finding predictable information in large databases and answer to questions that traditionally required intense manual analysis [4]. Due to its definition, data mining is applicable to educational processes, and an example of that is the emergence of a research branch named Educational Data Mining, in which patterns and prediction search techniques are used to find information that contributes to improving educational quality [5]. This paper presents a performance study of data mining algorithms: Decision Tree and Logistic Regression, applied to data generated by the academic function at a higher education institution. |
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