Data Mining to Databases: A Review Paper
Keywords:
Preprocessing, Clustering, Classification, Association Rule Mining, Anomaly DetectionAbstract
The modern era has witnessed a rapid surge in data generation, prompting the need for advanced analytical methods to efficiently process, analyze, and extract value from large volumes of data. In this context, this study aims to integrate data mining techniques into database systems to bolster their analytical capabilities and streamline decision-making processes. We focus on five key aspects: preprocessing, clustering, classification, association rule mining, and anomaly detection. Preprocessing ensures data quality and consistency, eliminating noise, and handling missing values. Clustering groups similar data points based on their attributes, facilitating pattern recognition and data segmentation. Classification categorizes data into predefined classes, enabling predictive modeling and improved understanding of relationships among data points. Association rule mining identifies frequent itemsets and generates rules to uncover relationships among variables, supporting business intelligence and decision-making.
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