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WIREs Data Mining Knowl Discov
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Incorporating domain ontology information into clustering in heterogeneous networks

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Abstract 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. This article is categorized under: Technologies > Structure Discovery and Clustering Algorithmic Development > Structure Discovery
An example of concept representation in an ontology
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Effect of μ on the number of central object (paper) clusters
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Influence of w on the number of central object (paper) clusters
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Estimating the density threshold of central objects in the running example
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Running example with unique identification numbers
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Star‐structure with four types of the running example
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Book ontology of the running example
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Star‐structure of heterogeneous data
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Algorithmic Development > Structure Discovery
Technologies > Structure Discovery and Clustering

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