Semantic Spectral Clustering with Contrastive Learning and Neighbor Mining

Abstract

Deep spectral clustering techniques are considered one of the most efficient clustering algorithms in data mining field. The similarity between instances and the disparity among classes are two critical factors in clustering fields. However, most current deep spectral clustering approaches do not sufficiently take them both into consideration. To tackle the above issue, we propose Semantic Spectral clustering with Contrastive learning and Neighbor mining (SSCN) framework, which performs instance-level pulling and cluster-level pushing cooperatively. Specifically, we obtain the semantic feature embedding using an unsupervised contrastive learning model. Next, we obtain the nearest neighbors partially and globally, and the neighbors along with data augmentation information enhance their effectiveness collaboratively on the instance level as well as the cluster level. The spectral constraint is applied by orthogonal layers to satisfy conventional spectral clustering. Extensive experiments demonstrate the superiority of our proposed frame of spectral clustering.