Semi-supervised deep embedded clustering

Abstract Clustering is an important topic in machine learning and data mining. Recently, deep clustering, which learns feature representations for clustering tasks using deep neural networks, has attracted increasing attention for various clustering applications. Deep embedded clustering (DEC) is one of the state-of-the-art deep clustering methods. However, DEC does not make use of prior knowledge to guide the learning process. In this paper, we propose a new scheme of semi-supervised deep embedded clustering (SDEC) to overcome this limitation. Concretely, SDEC learns feature representations that favor the clustering tasks and performs clustering assignments simultaneously. In contrast to DEC, SDEC incorporates pairwise constraints in the feature learning process such that data samples belonging to the same cluster are close to each other and data samples belonging to different clusters are far away from each other in the learned feature space. Extensive experiments on real benchmark data sets validate the effectiveness and robustness of the proposed method.