Part-in-whole retrieval (PWR) is an important problem in the field of computer-aided design (CAD) with applications in design reuse, feature recognition and suppression and so on. Initially, we present a non-parametric (and hence threshold independent) algorithm for segmenting CAD models (represented as meshes) which does not require any user intervention. As there is no labelled segmented dataset available for part clustering, we propose the use of autoencoders, one of the approaches used in deep networks along with hierarchical clustering. The features for autoencoder is derived from the Gauss map of the segments. The autoencoder network is then trained and validated using a hierarchical clustering-based approach that generates a dictionary of labels for each segment. PWR is then done by testing a query model with the network that retrieves models having the query as their subset. Comparison of the segmentation algorithm with the state-of-the-art approaches indicate that it performs better or on par. The algorithm was also tested for noisy models. Results of the part clustering and PWR are also presented for models from a CAD dataset along with the discussions. © 2019 Elsevier Ltd