In dynamic functional connectivity analysis, brain states can be derived by identifying repetitively occurring functional connectivity patterns. This presents a high-dimensional, unsupervised learning task, often approached with k-means clustering. To advance this, we use deep autoencoders for dimensionality reduction before applying k-means to the embedded space. We provide quantitative validation on synthetic data and demonstrate better performance than currently used approaches. We go on to demonstrate the utility of this method by applying it to real data from human subjects.