The star-structured high-order heterogeneous data is ubiquitous, such data represent objects of a certain type, connected to other types of data, or the features, so that the overall data schema forms a star-structure of inter-relationships. In this paper, we study the problem of co-clustering of star-structured high-order heterogeneous data. We present a new solution, a Hierarchical High-order Co-clustering Algorithm by Maximizing Modularity, MHCoC, which iteratively optimizes the objective function based on modularity and finally converges to a unique clustering result. In contrast to the traditional co-clustering methods, MHCoC merges information of multiple feature spaces of high-order heterogeneous data. Moreover, MHCoC takes a top-down strategy to perform a greedy divisive procedure, generating a tree-like hierarchical clustering result that reveal the relationship between clusters. To illustrate the process in more detail, we design a toy example to describe how MHCoC selects the appropriate co-cluster and splits it. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method.

星形结构的高阶异构数据无处不在，这种数据代表某种类型的对象，连接到其他类型的数据或特征，从而使整体数据模式形成相互关系的星形结构。在本文中，我们研究了星型结构高阶异构数据的共聚类问题。我们提出了一种新的解决方案，一种通过最大化模块化的分层高阶协同聚类算法 MHCoC，它基于模块化迭代优化目标函数，最终收敛到唯一的聚类结果。与传统的协同聚类方法相比，MHCoC 融合了高阶异构数据的多个特征空间的信息。此外，MHCoC 采用自上而下的策略来执行贪婪的分裂过程，生成一个树状层次聚类结果，揭示聚类之间的关系。为了更详细地说明该过程，我们设计了一个玩具示例来描述 MHCoC 如何选择合适的协同集群并对其进行拆分。对真实世界数据集的大量实验证明了所提出方法的有效性。