For the dataset with different sample contributions and different feature importance, it is difficult to acquire a proper cluster structure that covers the entire features of the sample set. To improve the clustering result, a novel weighted fuzzy clustering algorithm based on both samples and generalized entropy features, called SGEF‐WFCM, is proposed in this article, among which a new objective function is developed on the basis of feature‐weighted generalized entropy regularization with a double‐weighting strategy of samples and features, the weighted coefficients of the features to each cluster, as well as the importance of the samples to the cluster are calculated dynamically, to obtain a better clustering result. Finally, experiments on both synthetic datasets and real‐world datasets from UCI are employed to verify the performance of the proposed SGEF‐WFCM algorithm. The results show that SGEF‐WFCM is superior to the conventional FCM algorithm in both the effectiveness and the usefulness during practices.

中文翻译：

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具有样本和广义熵特征的双加权模糊聚类

对于具有不同样本贡献和不同特征重要性的数据集，很难获得覆盖样本集整个特征的合适聚类结构。为了改善聚类结果，提出了一种基于样本和广义熵特征的加权模糊聚类算法SGEF-WFCM，其中在特征加权广义熵正则化的基础上开发了一种新的目标函数。利用样本和特征的双重加权策略，可以动态计算特征对每个聚类的加权系数以及样本对聚类的重要性，以获得更好的聚类结果。最后，通过对UCI的合成数据集和真实数据集进行实验，以验证所提出的SGEF-WFCM算法的性能。结果表明，SGEF-WFCM在实践中的有效性和实用性均优于传统FCM算法。