Clustering is a fundamental task in machine learning, widely applied across various domains. This paper focuses on the clustering of attributed networks, which combine structural and attribute information. We introduce a novel model that unifies regularized data embedding and clustering, enhancing the representation and analysis of such networks. Our approach not only improves clustering performance for attributed network data but also demonstrates effectiveness in scenarios where the graph structure is not initially available. Through experimentation on benchmark datasets, we show that our method achieves superior performance in terms of key clustering external metrics. Furthermore, it provides relevant embeddings that simplify the identification of classes.