Selective multiple kernel fuzzy clustering with locality preserved ensemble

Chuanbin Zhang

doi:10.1016/j.knosys.2024.112327

Selective multiple kernel fuzzy clustering with locality preserved ensemble

2024-10-01

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DOI：10.1016/j.knosys.2024.112327

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Multiple kernel fuzzy clustering (MKFC) has demonstrated promising performance in capturing the nonlinear relationships within data. However, its effectiveness relies heavily on the appropriate selection of the fuzzification coefficient and kernel functions. To address this challenge, this paper proposes a novel clustering ensemble approach for improving the robustness and accuracy of the MKFC algorithm. The proposed method employs a multi-objective evolutionary optimization approach to heuristically select the optimal fuzzification and kernel coefficients. By employing the selected coefficients, the MKFC algorithm generates a diverse set of accurate candidate clustering results. Subsequently, a locality preserved ensemble mechanism is introduced to derive the final partition matrix, ensuring that all candidate clusterings within the Pareto non-dominated set contribute to the final consensus matrix. Moreover, this mechanism incorporates the knowledge about the locality of the dataset via a graph regularization term, thereby further enhancing the clustering performance. Comprehensive experiments conducted on widely adopted benchmark datasets demonstrate the superiority of the proposed method over the state-of-the-art approaches.

Keywords:

Clustering ensemble

Multiple kernel fuzzy clustering

Multi-objective optimization

Graph

Feature fusion

Selective multiple kernel fuzzy clustering with locality preserved ensemble

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