Deep variational mixture models (DVMMs) have demonstrated promising performance in unsupervised clustering for complicated high-dimensional data such as images. However, their prediction accuracy is often unstable and significantly influenced by randomness, particularly during the initialization of parameters. To reduce this uncertainty, we propose an ensemble approach that combines the predictions of multiple base models. Specifically, we introduce two individual ensemble strategies: voting and merging. In the voting strategy, the final label is determined by selecting the predicted class label with the most votes and lowest Shannon entropy. In the merging strategy, the class probability vectors (scaled by the temperature parameter) from different models are combined to predict the final class label. Experimental results on two image datasets demonstrate that these proposed methods yield reliable and superior clustering performance.