The existing algorithms are mostly unsupervised algorithms of a cluster ensemble, which cannot take advantages of known information of datasets. As a result, the precision, robustness, and stability of a cluster ensemble are degraded. To conquer these disadvantages, a semi-supervised cluster ensemble (SCE) model, based on both semi-supervised learning and ensemble learning technologies, is designed in this paper. There are three main works in this paper. The first is that SCE is proposed, and the variational inference oriented SCE is illustrated in detail. The second is based on the above work: an EM (expectation maximization) algorithm of SCE is illustrated step by step. The third is that some datasets are drawn from the UCI (University of California, Irvine) machine learning database for experiments which show that both SCE and its EM algorithm are good for semi-supervised cluster ensemble and outperforms NMFS (algorithm of nonnegative-matrix-factorization based semi-supervised), semi-supervised SVM (support vector machine), and LVCE (latent variable model for cluster ensemble). The Semi-Supervised Cluster Ensemble model is first stated in this paper, and this paper includes the advantages of both the semi-supervise learning and the cluster ensemble. Therefore, its result is better than the results of semi-learning clustering and cluster ensemble.