A data mining transmission switching heuristic for post-contingency AC power flow violation reduction in real-world, large-scale systems

Brown, William Eric; Moreno-Centeno, Erick

doi:10.1016/j.cor.2023.106391

Cited by 5 publications

(2 citation statements)

References 44 publications

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“…However, the sciences of data mining are used in a variety of social fields, including electronic learning, intelligent tutoring systems, text mining, and social network mining, among others. Due to this, the primary goal of the data mining application is to make accurate predictions using various types of mathematical algorithms [20]. On the other hand, research has shown that advancements in medical technology have made it possible to prevent and identify uterine cancer early [24].…”

Section: Discussionmentioning

confidence: 99%

“…The science of data mining has applications in a variety of social fields, including electronic learning, intelligent tutoring systems, text mining, and social network mining, among others. Due to this, the primary task of the data mining application is to make accurate predictions using various types of mathematical algorithms [20]. − Rules of association: the decision-tree algorithms are the fundamental idea of association rules.…”

Section: Theoretical Basismentioning

confidence: 99%

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Data mining for predictive analysis in gynecology: a focus on cervical health

Andrade-Arenas,

Rubio-Paucar,

Yactayo-Arias

2024

IJECE

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Currently, data mining based on the application of detection of important patterns that allow making decisions according to cervical cancer is a problem that affects women from the age of 24 years and older. For this purpose, the Rapid Miner Studio tool was used for data analysis according to age. To perform this analysis, the knowledge discovery in databases (KDD) methodology was used according to the stages that this methodology follows, such as data selection, data preparation, data mining and evaluation and interpretation. On the other hand, the comparison of methodologies such as the standard intersectoral process for data mining (Crips-dm), KDD and sample, explore, modify, model, evaluate (Semma) is shown, which is separated by dimensions and in each dimension both methodologies are compared. In that sense, a graph was created comparing algorithmic models such as naive Bayes, decision tree, and rule induction. It is concluded that the most outstanding result was -1.424 located in cluster 4 in the attribute result date.

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Section: Discussionmentioning

confidence: 99%

Section: Theoretical Basismentioning

confidence: 99%