The graph-information-based fuzzy clustering has shown promising results in various datasets. However, its performance is hindered when dealing with high-dimensional data due to challenges related to redundant information and sensitivity to the similarity matrix design. To address these limitations, this article proposes an implicit fuzzy k-means (FKMs) model that enhances graph-based fuzzy clustering for high-dimensional data. Instead of explicitly designing a similarity matrix, our approach leverages the fuzzy partition result obtained from the implicit FKMs model to generate an effective similarity matrix. We employ a projection-based technique to handle redundant information, eliminating the need for specific feature extraction methods. By formulating the fuzzy clustering model solely based on the similarity matrix derived from the membership matrix, we mitigate issues, such as dependence on initial values and random fluctuations in clustering results. This innovative approach significantly improves the competitiveness of graph-enhanced fuzzy clustering for high-dimensional data. We present an efficient iterative optimization algorithm for our model and demonstrate its effectiveness through theoretical analysis and experimental comparisons with other state-of-the-art methods, showcasing its superior performance.

中文翻译：

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IFKMHC：高维数据聚类的隐式模糊 K 均值模型


基于图信息的模糊聚类在各种数据集中显示出了有希望的结果。然而，由于冗余信息和对相似矩阵设计的敏感性相关的挑战，在处理高维数据时，其性能受到阻碍。为了解决这些限制，本文提出了一种隐式模糊 k 均值 (FKM) 模型，该模型增强了高维数据的基于图的模糊聚类。我们的方法没有明确设计相似度矩阵，而是利用从隐式 FKM 模型获得的模糊划分结果来生成有效的相似度矩阵。我们采用基于投影的技术来处理冗余信息，从而无需特定的特征提取方法。通过仅基于从隶属矩阵导出的相似度矩阵来制定模糊聚类模型，我们可以缓解诸如对初始值的依赖和聚类结果中的随机波动等问题。这种创新方法显着提高了高维数据的图增强模糊聚类的竞争力。我们为我们的模型提出了一种有效的迭代优化算法，并通过理论分析和与其他最先进方法的实验比较证明了其有效性，展示了其优越的性能。