Regression Method in Data Mining: A Systematic Literature Review

• Published in: Archives of computational methods in engineering 2024, Vol.31 (6), p.3515-3534
• Main Authors: Sebt, Mohammad Vahid, Sadati-Keneti, Yaser, Rahbari, Misagh, Gholipour, Zohreh, Mehri, Hamid
• Format: Article
• Language: English
• Subjects: Algorithms; Data mining; Engineering; Literature reviews; Mathematical and Computational Engineering; Regression; Review Article; Supervised learning
• ISSN: 1134-3060; 1886-1784
• DOI: 10.1007/s11831-024-10088-5

Regression is one of the most important supervised learning methods in data mining that is used to predict and discover knowledge in data mining science. After reviewing the studies conducted in the field of regression, it has been found that the tendency to use this method is increasing day by day among researchers. This study reviews 500 articles from about 230 reputable journals under one framework over the twenty-first century and also discusses the status and use of regression in data mining research. The systematic framework presented in this study includes the following steps: 1—Examining the position of regression in research conducted in data mining and determining the trend of different journals to conduct research in the field of regression in different years 2—Examining different study areas in the field of regression and determining the trend to conduct research in various areas of study in different years 3—Examining the algorithms used in the field of regression and determining the most widely used and trend to use algorithms by researchers in different years 4—Examining the keywords used in regression research in data mining and determining the strongest and most attractive rules obtained from the relationships of these keywords with each other using the Apriori algorithm.