A semi-supervised K-means clustering algorithm for multi-type relational data is proposed, which extends traditional K-means clustering by new methods of selecting initial clusters and similarity measures, so that it can semi-supervise cluster multi-type relational data. In order to achieve high performance, in the algorithm, besides attribute information, both labeled data and relationship information are employed. Experimental results on Movie database show the effectiveness of this method.