Deep Plug-and-Play Clustering with Unknown Number of Clusters

Abstract

Clustering is an essential task for the purpose that data points can be classified in an unsupervised manner. Most deep clustering algorithms are very effective when given the number of clusters K. However, when K is unknown, finding the appropriate K for these algorithms can be computationally expensive via model-selection criteria, and applying algorithms with an inaccurate K can hardly achieve the state-of-the-art performance. This paper proposes a plug-and-play clustering module to automatically adjust the number of clusters, which can be easily embedded into existing deep parametric clustering methods. By analyzing the goal of clustering, a split-and-merge framework is introduced to reduce the intra-class diversity and increase the inter-class difference, which leverages the entropy between different clusters. Specifically, given an initial clustering number, clusters can be split into sub-clusters or merged into super-clusters and converge to a stable number of K clusters at the end of training. Experiments on benchmark datasets demonstrate that the proposed method can achieve comparable performance with the state-of-the-art works without requiring the number of clusters.