Deep Clustering based on Bi-Space Association Learning
Hao Huang, Shinjae Yoo, Chenxiao Xu · 2021
Clustering is the task of instance grouping so that similar ones are grouped into the same cluster, while dissimilar ones are in different clusters. However, such similarity is a local concept in regard to different clusters and their relevant feature space. This work aims to discover clusters by exploring feature association and instance similarity concurrently. We propose a deep clustering framework that can localize the search for relevant features appertaining to different clusters. In turn, this allows for measuring instance similarity that exist in multiple, possibly overlapping, feature subsets, which contribute to more accurate clustering of instances. Additionally, the relevant features of each cluster endow interpretability of clustering results. Experiments on text and image datasets show that our method outperforms existing state-of-the-art baselines.