  We introduce a novel self-supervised deep clustering approach tailored for
unstructured data without requiring prior knowledge of the number of clusters,
termed Adaptive Self-supervised Robust Clustering (ASRC). In particular, ASRC
adaptively learns the graph structure and edge weights to capture both local
and global structural information. The obtained graph enables us to learn
clustering-friendly feature representations by an enhanced graph auto-encoder
with contrastive learning technique. It further leverages the clustering
results adaptively obtained by robust continuous clustering (RCC) to generate
prototypes for negative sampling, which can further contribute to promoting
consistency among positive pairs and enlarging the gap between positive and
negative samples. ASRC obtains the final clustering results by applying RCC to
the learned feature representations with their consistent graph structure and
edge weights. Extensive experiments conducted on seven benchmark datasets
demonstrate the efficacy of ASRC, demonstrating its superior performance over
other popular clustering models. Notably, ASRC even outperforms methods that
rely on prior knowledge of the number of clusters, highlighting its
effectiveness in addressing the challenges of clustering unstructured data.
