A new algorithm was developed to improve clustering of high-dimensional data. By using a selective ensemble approach based on semi-supervised K-means clustering, the algorithm can filter out low-quality clustering results and achieve better overall clustering solutions. Through extensive experiments, it was found that this new algorithm outperformed other clustering methods significantly in terms of performance.