Open AccessReview of techniques, tools, algorithms and attributes for data mining used in student desertion
Author(s) -
Kely Yineth Diaz Pedroza,
Brenda Yurimar Chindoy Chasoy,
Ariel Gómez
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1409/1/012003
Subject(s) - c4.5 algorithm , desertion , computer science , class (philosophy) , selection (genetic algorithm) , data science , data mining , machine learning , artificial intelligence , naive bayes classifier , political science , support vector machine , law
This article makes a review of the application of data mining in the academic desertion of the students; with the aim of finding common elements used by different authors about desertion. The search of the articles was carried out in digital libraries, indexed journals, institutional repositories among others. The selection criteria were based on the depth of the techniques, algorithms, tools and attributes used in the publication. Among the results we have that most of the researches are related are supervised learning, we have the classification as the most applied technique and we use the algorithm j48 and the variable or class to predict as the academic state.