Adaptive Data Mining Framework for Detecting Hidden Patterns in Large-Scale Heterogeneous Data

Abstract

Many traditional data mining techniques are challenged by the explosion of large-scale heterogeneous data from a wide range of sources, such as structured databases, unstructured text, sensor streams, and graph-structured networks. Traditional methods typically make the assumption of uniform data distribution and a fixed environment, which is not able to capture complex, latent patterns that change with respect to time. In this paper, an Adaptive Data Mining Framework (ADMF) is proposed to discover the hidden patterns in large-scale heterogeneous data by dynamically integrating multi-source information, adaptive concept-drift handling and hybrid pattern discovery mechanisms. The framework is based on recent developments in heterogeneous graph neural networks, adaptive ensemble learning and multi-stage pattern mining, and comprises four major components: (1) heterogeneous data fusion/representation learning, (2) adaptive drift detection and model updating, (3) multi-view contextual pattern mining, and (4) interpretable knowledge extraction. A conceptual evaluation using current literature proves the framework potential to enhance the accuracy, scalability, and robustness of pattern discovery in different application areas like multi-energy systems, network security and enterprise analytics. The paper concludes by identifying gaps in the existing methods, specifying clear objectives and stating a methodology that provides a balance between theory and practice. Future research and practical implications are discussed.