In the era of Industry 4.0, single-view clustering algorithm is difficult to play a role in the face of complex data, i.e., multiview data. In recent years, an extension of the traditional single-view clustering is multiview clustering technology, which is becoming more and more popular. Although the multiview clustering algorithm has better effectiveness than the single-view clustering algorithm, almost all the current multiview clustering algorithms usually have two weaknesses as follows. (1) The current multiview collaborative clustering strategy lacks theoretical support. (2) The weight of each view is averaged. To solve the above-mentioned problems, we used the Havrda-Charvat entropy and fuzzy index to construct a new collaborative multiview fuzzy c-means clustering algorithm using fuzzy weighting called Co-MVFCM. The corresponding results show that the Co-MVFCM has the best clustering performance among all the comparison clustering algorithms.

中文翻译：

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工业4.0时代基于熵的多视图数据聚类分析

在工业4.0时代，面对复杂数据（即多视图数据），单视图聚类算法很难发挥作用。近年来，传统的单视图聚类的扩展是多视图聚类技术，它越来越受欢迎。尽管多视图聚类算法比单视图聚类算法具有更好的效果，但是几乎所有当前的多视图聚类算法通常都具有以下两个缺点。（1）当前的多视图协作聚类策略缺乏理论支持。（2）平均每个视图的权重。为了解决上述问题，我们利用Havrda-Charvat熵和模糊指数，构造了一种新的基于模糊加权的协同多视图模糊c-均值聚类算法，称为Co-MVFCM。