Advancing Database Mining: Integrative Approaches of Machine & Deep Learning for Sequential Pattern Analysis

Abstract

In the vast expanse of data science, database mining has secured itself as an essential thread, allowing organizations to weave patterns amidst billows of bytes. This study examines the combination of machine learning and deep learning methods for heavy-duty sequential pattern discovery in database mining applications. The first targets are to investigate whether it is more efficient to extract sequential patterns using hybrid frameworks combining traditional machine learning algorithms with other deep learning architectures, detect suitable methods for developing efficient hybrid frameworks for SP extraction, and evaluate effectiveness against efficiency in different database environments. I applied a mixed-methods research design using secondary data analysis from benchmark databases including UCI Machine Learning Repository datasets. Finally, we propose that such integrative approaches (using ML preprocessing pipeline for data preparation and DL for pattern recognition) can provide better accuracy comparing to DL alone approaches. Using the hybrid Long Short-Term Memory networks combined with Random Forest preprocessing, results show sequential pattern detection accuracy of 94.7%, a 12.3% improvement compared to using Long Short-Term Memory networks and Random Forest alone. Explains how attention mechanisms improve recognition of patterns in sequences over time. It is concluded that combinational machine and deep learning frameworks are the best way forward for enhancing the database mining of sequential pattern analysis applied settings.