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Convex fuzzy k-medoids clustering

机译：凸模糊k-yemoids聚类

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K-medoids clustering is among the most popular methods for cluster analysis despite its use requiring several assumptions about the nature of the latent clusters. In this paper, we introduce the Convex Fuzzy k-Medoids (CFKM) model, which not only relaxes the assumption that objects must be assigned entirely to one and only one medoid, but also that medoids must be assigned entirely to one and only one cluster. The resulting model is convex, thus its resolution is completely robust to initialization. To illustrate the usefulness of the CFKM model, we compare it with two fuzzy k-medoids clustering models: the Fuzzy k-Medoids (FKM) and the Fuzzy Clustering with Multi-Medoids (FMMdd), both solved approximately by heuristics because of their hard computational complexity. Our experiments with both synthetic and real-world data as well as a user survey reveal that the model is not only more robust to the choice hyperparameters of the fuzzy clustering task, but also that it can uniquely discover important aspects of data inherently fuzzy in nature. (C) 2020 Elsevier B.V. All rights reserved.
机译：k-medoids聚类是群集分析的最流行方法之一，尽管它需要几个关于潜在群集的性质的若干假设。在本文中，我们介绍了凸模糊k-myoids（CFKM）模型，它不仅放宽了对象必须完全分配给一个且只有一个拍摄的假设，而且必须完全分配给一个群集的METOIDS 。得到的模型是凸的，因此其分辨率是完全鲁棒的初始化。为了说明CFKM模型的有用性，我们将其与两种模糊K-METOIDS聚类模型进行比较：模糊k-yemoids（Fkm）和具有多麦多斯（FMMDD）的模糊聚类，因为它们很难解决计算复杂性。我们的实验与综合性和现实世界数据以及用户调查显示，该模型不仅对模糊聚类任务的选择超参数更加强大，而且还可以独特地发现自然模糊的数据的重要方面。 （c）2020 Elsevier B.v.保留所有权利。

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