01665nas a2200241   4500000000100000000000100001008004100002260001200043653004500055653002600100653002200126653001800148653002100166100003000187700002000217700002300237245008700260856008100347300001000428490000600438520096500444022001401409       2022                            d  c03/202210aBusiness Analysis With Expert Assessment10aBusiness Intelligence10aCandidate Product10aICT Ecosystem10aMachine Learning1 aFulgencio Sánchez-Torres1 aIván González1 aCosmin C. Dobrescu00aMachine Learning in Business Intelligence 4.0: Cost Control in a Destination Hotel  uhttps://www.ijimai.org/journal/sites/default/files/2022-02/ijimai7_3_8_0.pdf  a86-950 v73 aCost control is a recurring problem in companies where studies have provided different solutions. The main objective of this research is to propose and validate an alternative to cost control using data science to support decision-making using the business intelligence 4.0 paradigm. The work uses Machine Learning (ML) to support decision-making in company cost-control management. Specifically, we used the ability of hierarchical agglomerative clustering (HAC) algorithms to generate clusters and suggest possible candidate products that could be substituted for other, more cost-effective ones. These candidate products were analyzed by a panel of company experts, facilitating decisions based on business costs. We needed to analyze and modify the company's ecosystem and its associated variables to obtain an adequate data warehouse during the study, which was developed over three years and validated HAC as a support to decision-making in cost control.  a1989-1660