Knowledge-Aware Clustering

Data clustering aims to partition the input data entities into several disjoint categories, where similar entities are grouped together while dissimilar ones are pulled apart. In general, the existing data clustering methods merely depend on the attribute information or constructed similarity graph information of the input data entities, and such one-sided cues may result in an incomplete understanding and potentially biased conclusions. How to well consider the inherent heterogeneous knowledge of data while maintaining the local semantics remains a challenging problem. To address this, we propose a novel knowledge-aware clustering (KAC) method, where the attribute information and inherent heterogeneous knowledge of data are jointly considered for better cluster structure recovery. Within this framework, there are four major modules. They are: 1) the target attribute autoencoder to capture a customizable target feature representation; 2) the meta-path aggregated knowledge graph encoder to learn an informative graph feature representation; 3) the dual information maximization to ensure the consistent semantics between two embedding representations; and 4) the self-training clustering to self-supervise the clustering learning. Experiments on several real-world datasets are conducted to validate the superiority of KAC, indicating the significance of explicitly considering the heterogeneous knowledge while maintaining local semantics.