A Review Paper on Data Mining Methods and their Applications

Abstract

This research paper provides a comprehensive review of data mining methods and their applications. Over the past decade, data mining has emerged as a crucial tool in extracting useful patterns and insights from large datasets across various domains. The paper begins by outlining the foundational principles and techniques of data mining, including classification, clustering, association rule mining, and anomaly detection. It then proceeds to explore the evolution and advancements of these techniques throughout the specified timeframe, highlighting key developments, methodologies, and challenges encountered in their application. Furthermore, the paper discusses notable applications of data mining in real-world scenarios such as healthcare, finance, telecommunications, and e-commerce. These applications demonstrate the versatility and effectiveness of data mining in solving complex problems, improving decision-making processes, and enhancing business intelligence. Moreover, the review addresses the impact of technological advancements and the proliferation of digital data on the field of data mining during the specified period. By synthesizing insights from existing literature and research studies, this review aims to provide researchers, practitioners, and stakeholders with a comprehensive understanding of the landscape of data mining methods and their practical implications.