Clustering of structure-rich heterogeneous information networks composed of multiple types of objects and relationships, which has become a challenge in data mining. Most of the existing clustering heterogeneous network methods focus on the internal information of the dataset while ignoring the domain knowledge outside the dataset. However, in real-world scenarios, domain knowledge can often offer valuable information for clustering. In this study, we propose a three-layer model OntoHeteClus, which is able to cluster multitype objects in star-structured heterogeneous networks by considering both the dataset itself and the background information quantified via the ontology. OntoHeteClus first evaluates the similarity between central objects according to formalized domain ontology information, based on which central objects are subsequently clustered. Finally, attribute objects are clustered according to the central object clustering result. A numerical example is presented to illustrate the modeling concept and working principle of the proposed method, and experiments on a real-world dataset demonstrate the effectiveness of the proposed algorithms.

中文翻译：

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将领域本体信息纳入异构网络中的聚类

对由多种类型的对象和关系组成的结构丰富的异构信息网络进行聚类，这已成为数据挖掘中的一个挑战。现有的聚类异构网络方法大多关注数据集的内部信息，而忽略了数据集外部的领域知识。然而，在现实世界的场景中，领域知识通常可以为聚类提供有价值的信息。在这项研究中，我们提出了一个三层模型 OntoHeteClus，它能够通过考虑数据集本身和通过本体量化的背景信息来聚类星形结构异构网络中的多类型对象。OntoHeteClus 首先根据形式化的领域本体信息评估中心对象之间的相似性，基于哪些中心对象随后被聚类。最后，根据中心对象聚类结果对属性对象进行聚类。给出了一个数值例子来说明所提出方法的建模概念和工作原理，并且在真实世界数据集上的实验证明了所提出算法的有效性。