Análisis comparativo entre algoritmos de aprendizaje de reglas para identificar indicadores que influyen en el bajo rendimiento industrial
Year: 2022
Type: article
Abstract: The computerization of the processes of the sugar industry generates abundant data. At present, the application of the programs of the existing Agro-Industrial Platform in AZCUBA has guaranteed the speed and quality of harvest information and the benefits derived from it. The cuban sugar industry needs to implement scientific tools and methods that allow the influence of the technological variables of the industrial process on the efficiency of cane sugar manufacturing to be analyzed and quantified with greater precision. For this reason, it is necessary to discover what are the main causes that are influencing the low industrial yields in the cane sugar manufacturing process in cuba based on the historical data of the sugar harvest. The CRISP-DM methodology is used for modeling the data mining process. As a starting point for deeper analysis, a comparison between rule learning algorithms is made, where patterns that influence low industrial yields are obtained.
Institution: Polytechnic José Antonio Echeverría
Language: Spanish
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7219972856