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    This study introduces a novel rule-based modeling approach using density-based spatial clustering of applications with noise (DBSCAN) to improve accuracy in complex systems. Models built from DBSCAN subgranules demonstrated superior performance in analyzing system behaviors.

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    Area of Science:

    • Computational intelligence
    • Data mining
    • Machine learning

    Background:

    • Rule-based models are effective for complex, nonlinear systems but can suffer from accuracy issues due to poor rule construction.
    • Information granules are crucial for rule-based modeling, and their quality directly impacts model performance.
    • Traditional methods like fuzzy C-means clustering for granule formation have limitations.

    Purpose of the Study:

    • To propose and evaluate a new rule-based modeling approach using density-based spatial clustering of applications with noise (DBSCAN) for improved rule construction.
    • To investigate two distinct methods of constructing rules from DBSCAN-generated data structures.
    • To compare the performance of the proposed DBSCAN-based models against a traditional fuzzy C-means-based model.

    Main Methods:

    • Utilized density-based spatial clustering of applications with noise (DBSCAN) to generate data structures for information granules.
    • Developed two rule-based models: one directly using DBSCAN clusters and another using subgranules within clusters.
    • Compared experimental results with a conventional model employing fuzzy C-means-based granules.

    Main Results:

    • The rule-based model constructing rules from subgranules within DBSCAN structures achieved the best performance.
    • DBSCAN-based information granules offer an effective alternative to traditional fuzzy C-means for rule-based modeling.
    • The proposed approach enhances the analysis of complex system behaviors.

    Conclusions:

    • Rule-based modeling can be significantly improved by employing DBSCAN for information granule generation and rule construction.
    • Generating subgranules within DBSCAN clusters provides a more effective strategy for rule formation compared to direct cluster usage.
    • The DBSCAN-based approach offers a robust and accurate method for modeling complex and nonlinear systems.