Exploring Data Mining Techniques in Machine Learning : A Comprehensive Review

Abstract

In the era of big data, extracting meaningful insights from vast datasets has become a critical challenge, prompting the integration of data mining techniques with machine learning methodologies. This paper provides a comprehensive review of various data mining techniques employed in the field of machine learning. By delving into classification, clustering, association rule mining, regression, dimensionality reduction, and anomaly detection, we aim to offer a thorough understanding of these methodologies and their applications. The paper further explores the integration of data mining techniques within machine learning workflows, showcasing their collective impact on enhancing predictive modeling and knowledge discovery. Real-world applications across diverse domains underscore the versatility and practical significance of these techniques. Additionally, challenges in implementation are discussed, along with potential areas for future research and advancements. This comprehensive review serves as a valuable resource for researchers, practitioners, and enthusiasts seeking a deeper understanding of the nuanced landscape of data mining techniques in the context of machine learning.