Semi-supervised cluster ensemble based on binary similarity matrix

The paper introduces a semi-supervised cluster ensemble of pairwised constrains based on the binary similarity matrix. Pairwised constrains are the typical way of semi-supervised learning. Cluster ensemble can increase robustness of clustering and it is helpful for knowledge reuse and distributed computing. The existing algorithms are mostly unsupervised algorithms of cluster ensemble which can´t take advantages of known information ofdatasets. As a result the precision, robustness and stability of cluster ensemble are degraded. Semi-supervised cluster ensemble may conquer these disadvantages. The idea is that we use pairwised constrains as semi-supervised learning for semi-supervised cluster ensemble, in this paper there are three works presented. First, we state a semi-supervised cluster ensemble method. Second, the model of semi-supervised cluster ensemble is illustrated in detail. Third, some UCI datasets are chosen for the experiments, and the results show that semi-supervised cluster ensemble works well.