Fuzzy cluster ensemble is an important research component of ensemble learning, which is used to aggregate several fuzzy base clusterings to generate a single output clustering with improved robustness and quality. However, since clustering is unsupervised, where “accuracy” does not have a clear meaning, it is difficult for existing ensemble methods to integrate multiple fuzzy k-means clusterings to find arbitrarily shaped clusters. To overcome the deficiency, we propose a new ensemble clusterer (algorithm) of multiple fuzzy k-means clusterings based on a local hypothesis. In the new algorithm, we study the extraction of local-credible memberships from a base clustering, the production of multiple base clusterings with different local-credible spaces, and the construction of cluster relation based on indirect overlap of local-credible spaces. The proposed ensemble clusterer not only inherits the scalability of fuzzy k-means but also overcomes the inability to find arbitrarily shaped clusters. We compare the proposed algorithm with other cluster ensemble algorithms on several synthetical and real datasets. The experimental results illustrate the effectiveness and efficiency of the proposed algorithm.

中文翻译：

---

多个模糊 k 均值聚类的集成聚类器，用于识别任意形状的聚类


模糊聚类集成是集成学习的重要研究组成部分，用于聚合多个模糊基聚类以生成具有改进的鲁棒性和质量的单个输出聚类。然而，由于聚类是无监督的，其中“准确性”没有明确的含义，现有的集成方法很难集成多个模糊k均值聚类来找到任意形状的聚类。为了克服这一缺陷，我们提出了一种基于局部假设的多个模糊 k 均值聚类的新集成聚类器（算法）。在新算法中，我们研究了从基聚类中提取局部可信隶属度、具有不同局部可信空间的多个基聚类的产生以及基于局部可信空间间接重叠的聚类关系的构造。所提出的集成聚类器不仅继承了模糊 k 均值的可扩展性，而且克服了无法找到任意形状聚类的问题。我们在几个综合数据集和真实数据集上将所提出的算法与其他集群集成算法进行了比较。实验结果说明了该算法的有效性和效率。