Design and Implementation of an Integrated Data Mining Framework for Efficient Knowledge Discovery in Large-Scale Data Warehousing Systems
Keywords:
Data Mining, Data Warehousing, Knowledge Discovery, Big Data, Hybrid Algorithms, Modular Architecture, OLAP, ETL, ScalabilityAbstract
The exponential growth of data across various sectors necessitates the integration of advanced data mining techniques within data warehousing systems. This paper presents a novel, scalable framework designed to unify the processes of data collection, preprocessing, mining, and interpretation to extract actionable knowledge. By leveraging hybrid algorithms and a modular architecture, the proposed framework enhances both performance and scalability, making it suitable for enterprise-level deployments. The study outlines the architecture, implementation, and evaluation of the framework through empirical experiments on benchmark datasets.
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Copyright (c) 2025 Chike Obi (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
