Hybrid Grid Partitioning, Rough Set Theory, and Fuzzy Rule Generation for Enhanced Association Rule Mining on Complex Datasets - Neeta Grant Caribbean (International Enterprise Integration Association, 2020)
Association rule mining plays a crucial role in extracting valuable insights and patterns from complex datasets. However, traditional association rule mining algorithms often face challenges in accurately discovering relevant rules due to the complexity, uncertainty, and vagueness inherent in such datasets. In this research, we propose an integrated approach that combines hybrid grid partitioning, rough set theory, and fuzzy rule generation to enhance association rule mining on complex datasets. First, hybrid grid partitioning is employed to divide the data space into a set of refined grid cells, allowing for more precise representation of the dataset's structure. Next, rough set theory is utilized to handle uncertainty and vagueness by computing lower and upper approximations of concepts. This enables the identification of objects that share similar condition attribute values and improves the robustness of rule generation. Additionally, fuzzy rule generation is incorporated to capture nuanced relationships and patterns within the dataset. Fuzzy logic is employed to represent imprecise and subjective concepts, facilitating the discovery of deeper insights and enhancing the comprehensibility of the generated rules. The proposed approach contributes to the accuracy, interpretability, and relevance of association rules in complex datasets. By integrating multiple techniques, it addresses the limitations of traditional algorithms and provides a comprehensive framework for knowledge discovery. Experimental evaluations demonstrate the effectiveness of the proposed approach in enhancing rule discovery accuracy and interpretability compared to traditional methods. Although some limitations, such as scalability and parameter sensitivity, need to be addressed, the research's findings highlight the potential of the integrated approach for extracting valuable insights from complex datasets. The proposed methodology has broad applicability across various domains and can empower decision-making processes in areas such as market basket analysis, customer behavior analysis, bioinformatics, and web mining. Future research can focus on addressing the identified limitations and further validating the approach's effectiveness in real-world scenarios.