Data Mining System and Applications: A Review

The rapid expansion of digital data has led to an increased need for efficient and intelligent data mining techniques capable of handling both structured and semi-structured data. Structured data, typically found in relational databases, and semi-structured data, such as XML, JSON, and web documents, pose unique challenges due to their varying formats, schema flexibility, and data complexity. This research explores and compares advanced data mining methodologies tailored to extract valuable patterns, insights, and knowledge from both data types. The study examines classification, clustering, association rule mining, and anomaly detection techniques, highlighting their adaptability and performance across diverse datasets. Special attention is given to preprocessing strategies, integration frameworks, and hybrid mining models that can effectively bridge the gap between rigidly structured and flexible semi-structured data. Through experimental evaluation on real-world datasets, the thesis demonstrates how hybrid and adaptive models improve accuracy, scalability, and interpretability. The proposed framework aims to support data-driven decision-making in fields such as healthcare, finance, e-commerce, and IoTbased systems. By providing a comprehensive understanding of mining techniques and their practical implementations, this work contributes to the advancement of intelligent data processing in heterogeneous information environments.