Mathematical and Statistical Methods in the Application Fields of Data Mining Technology
DOI:
https://doi.org/10.69760/lumin.2025003006Keywords:
data mining, statistics, linear algebra, optimization, machine learningAbstract
In today’s digital age, enormous volumes of data are generated every moment. Data mining leverages mathematical and statistical methods—alongside machine learning, database management, and data visualization—to extract valuable knowledge from these large data sets. Its techniques are applied successfully in diverse areas, including business, government, healthcare, science, and sports, with use cases such as database marketing, fraud detection, retail analytics, credit scoring, astronomy, and molecular biology. Mathematics provides the backbone of these processes through statistics, optimization, linear algebra, probability theory, and other fields. Together, mathematical and statistical methods enable efficient preprocessing, accurate modeling, reliable forecasting, and optimization. Their integration makes large‑scale data easier to process and analyze, ultimately supporting informed decision‑making across multiple domains.
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