Intelligent Data Mining Frameworks for Knowledge Discovery and Analysis

Richi Nayak

Intelligent Data Mining Frameworks for Knowledge Discovery and Analysis

Author(s)	Richi Nayak
Country	Australia
Abstract	The rapid expansion of digital information has created an increasing demand for intelligent data mining frameworks capable of transforming large and complex datasets into meaningful knowledge. Data mining provides systematic techniques for discovering hidden patterns, relationships, trends, and anomalies within structured and unstructured information. However, conventional data mining approaches may encounter limitations when dealing with high-dimensional datasets, heterogeneous data sources, nonlinear relationships, real-time information, and rapidly changing environments. Intelligent data mining frameworks address these challenges by integrating statistical analysis, machine learning, artificial intelligence, pattern recognition, database technologies, and automated analytical processes. This study examines the role of intelligent data mining frameworks in knowledge discovery and analytical decision-making. A qualitative descriptive research design based on secondary academic literature was adopted to examine major data mining techniques, knowledge discovery processes, analytical capabilities, and implementation challenges. The analysis focuses on classification, clustering, association-rule mining, anomaly detection, predictive analytics, feature selection, and intelligent pattern recognition. The findings indicate that intelligent data mining frameworks can enhance knowledge discovery by improving pattern identification, predictive capability, analytical efficiency, and decision-support quality. Machine learning integration enables mining systems to adapt to complex datasets, while intelligent preprocessing and feature-selection mechanisms can improve the quality of discovered knowledge. Nevertheless, data quality, privacy, computational requirements, interpretability, algorithmic bias, scalability, and changing data distributions remain significant challenges. The study concludes that intelligent data mining provides a strong foundation for modern knowledge discovery when analytical methods are integrated with appropriate data governance, validation, domain expertise, and responsible computational practices. Future frameworks should emphasize adaptive learning, explainable mining, real-time analytics, privacy-preserving approaches, and human-centered interpretation to improve the reliability and practical value of knowledge discovered from increasingly complex data environments.
Keywords	Data Mining, Knowledge Discovery, Intelligent Data Mining, Machine Learning, Pattern Recognition, Data Analytics, Predictive Analytics, Classification, Clustering, Decision Support.
Field	Engineering
Published In	Volume 1, Issue 6, November-December 2019
Published On	2019-12-30

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Intelligent Data Mining Frameworks for Knowledge Discovery and Analysis

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