Exploration of Data Mining Techniques in Business Decision-Making
DOI:
https://doi.org/10.46576/ijsseh.v5i2.4647Keywords:
Data Mining, Decision Making, Classification, Clustering, Association, Literature StudyAbstract
This research examines the use of data mining techniques in business decision-making. By analyzing various data mining methods such as classification, clustering, and association, the study demonstrates how data mining can enhance operational efficiency and marketing strategies. The literature review provides insights into the practical applications and benefits of data mining across different industries. The research highlights the potential of data mining to uncover hidden patterns and trends in large datasets, which can be used to make more informed and timely business decisions. Additionally, the study identifies key challenges in implementing data mining, such as data integration, selecting appropriate algorithms, and interpreting results. The findings are expected to offer practical guidance for companies aiming to leverage data mining in their operations. By understanding the advantages and applications of data mining, businesses can improve their decision-making processes, optimize resource allocation, and develop more effective strategies. This research serves as a valuable resource for organizations looking to harness the power of data mining to gain a competitive edge in the market.
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