Data Mining Techniques for Knowledge Discovery and Predictive Analysis in Large-Scale Database
Keywords:
Data Mining, Knowledge Discovery, Predictive Analysis, Large-Scale Database, Classification, Clustering, Association Rule Mining, Machine Learning, Database Management, Decision Support SystemsAbstract
The rapid growth of digital information has increased the importance of data mining techniques for extracting meaningful knowledge and supporting predictive analysis from large-scale databases. Organizations across various sectors generate enormous volumes of structured and unstructured data through business transactions, customer interactions, financial activities, healthcare systems, and digital platforms. However, the availability of large quantities of data does not automatically generate useful information. Advanced analytical techniques are required to identify hidden patterns, relationships, trends, and predictive insights. This study examines the role of data mining techniques in knowledge discovery and predictive analysis, emphasizing classification, clustering, association rule mining, regression, decision trees, neural networks, and statistical learning. It explores the processes involved in data collection, preprocessing, transformation, algorithm selection, model development, and evaluation. Particular attention is given to the challenges of scalability, data quality, computational complexity, model accuracy, and information security in large-scale database environments. The study also discusses how predictive models support organizational decision-making, operational efficiency, customer relationship management, and strategic planning. By examining established research and methodological approaches, this paper highlights the importance of integrating data mining algorithms with effective database management systems to transform extensive datasets into reliable, actionable, and valuable knowledge
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