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    This study introduces the Knowledge-induced Multiple Kernel Fuzzy Clustering (KMKFC) algorithm, enhancing fuzzy clustering with domain knowledge. The novel approach improves knowledge extraction and data mapping for superior clustering performance.

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    Area of Science:

    • Artificial Intelligence
    • Data Science
    • Machine Learning

    Background:

    • Fuzzy clustering methods benefit from domain knowledge, leading to knowledge-driven and data-driven approaches.
    • Existing methods face challenges in knowledge extraction and fusion mechanisms.
    • There is a need for improved fuzzy clustering algorithms that effectively integrate domain knowledge.

    Purpose of the Study:

    • To propose a novel Knowledge-induced Multiple Kernel Fuzzy Clustering (KMKFC) algorithm.
    • To enhance knowledge extraction and fusion in fuzzy clustering.
    • To improve the adaptability and performance of fuzzy clustering through multiple kernels and domain knowledge integration.

    Main Methods:

    • Developed the Relative Density-based Knowledge Extraction (RDKE) method for extracting high-density knowledge points and initializing cluster centers.
    • Introduced a multiple kernel mechanism to improve data mapping to high-dimensional space and enhance clustering adaptability.
    • Integrated extracted knowledge points into the KMKFC algorithm via a knowledge-influence matrix to guide the iterative clustering process.
    • Proposed RDKE with Automatic knowledge acquisition (RDKE-A) and KMKFC-A for automated knowledge point generation.
    • Proved the convergence of both KMKFC and KMKFC-A algorithms.

    Main Results:

    • The proposed KMKFC and KMKFC-A algorithms demonstrated superior performance compared to thirteen other algorithms.
    • Performance was evaluated using four standard evaluation indexes and convergence speed.
    • The RDKE method effectively extracts relevant knowledge points for initializing cluster centers.
    • The multiple kernel mechanism improved the algorithm's ability to discover data differences.

    Conclusions:

    • The KMKFC and KMKFC-A algorithms represent a significant advancement in knowledge-driven fuzzy clustering.
    • The methods effectively address limitations in knowledge extraction and fusion.
    • The proposed algorithms offer improved clustering accuracy and faster convergence rates, validated by experimental studies.