Clustering ensemble integrates multiple base clustering results to obtain a consensus result and thus improves the stability and robustness of the single clustering method. Since it is natural to use a hypergraph to represent the multiple base clustering results, where instances are represented by nodes and base clusters are represented by hyperedges, some hypergraph based clustering ensemble methods are proposed. Conventional hypergraph based methods obtain the final consensus result by partitioning a pre-defined static hypergraph. However, since base clusters may be imperfect due to the unreliability of base clustering methods, the pre-defined hypergraph constructed from the base clusters is also unreliable. Therefore, directly obtaining the final clustering result by partitioning the unreliable hypergraph is inappropriate. To tackle this problem, in this paper, we propose a clustering ensemble method via structured hypergraph learning, i.e., instead of being constructed directly, the hypergraph is dynamically learned from base results, which will be more reliable. Moreover, when dynamically learning the hypergraph, we enforce it to have a clear clustering structure, which will be more appropriate for clustering tasks, and thus we do not need to perform any uncertain postprocessing, such as hypergraph partitioning. Extensive experiments show that, our method not only performs better than the conventional hypergraph based ensemble methods, but also outperforms the state-of-the-art clustering ensemble methods.

Clustering ensemble 将多个基类的聚类结果进行整合以获得一致的结果，从而提高了单一聚类方法的稳定性和鲁棒性。由于使用超图来表示多个基聚类结果是很自然的，其中实例由节点表示，基簇由超边表示，因此提出了一些基于超图的聚类集成方法。传统的基于超图的方法通过划分预定义的静态超图来获得最终的共识结果。然而，由于基聚类方法的不可靠性，基簇可能不完美，从基簇构建的预定义超图也不可靠。因此，直接通过对不可靠的超图进行分区来获得最终的聚类结果是不合适的。为了解决这个问题，在本文中，我们提出了一种通过结构化超图学习的聚类集成方法，即不是直接构造超图，而是从基础结果中动态学习超图，这将更加可靠。此外，在动态学习超图时，我们强制它具有清晰的聚类结构，这将更适合聚类任务，因此我们不需要执行任何不确定的后处理，例如超图分区。大量实验表明，我们的方法不仅比传统的基于超图的集成方法性能更好，而且优于最先进的聚类集成方法。超图是从基础结果中动态学习的，这将更可靠。此外，在动态学习超图时，我们强制它具有清晰的聚类结构，这将更适合聚类任务，因此我们不需要执行任何不确定的后处理，例如超图分区。大量实验表明，我们的方法不仅比传统的基于超图的集成方法性能更好，而且优于最先进的聚类集成方法。超图是从基础结果中动态学习的，这将更可靠。此外，在动态学习超图时，我们强制它具有清晰的聚类结构，这将更适合聚类任务，因此我们不需要执行任何不确定的后处理，例如超图分区。大量实验表明，我们的方法不仅比传统的基于超图的集成方法性能更好，而且优于最先进的聚类集成方法。