  Finding well-defined clusters in data represents a fundamental challenge for
many data-driven applications, and largely depends on good data representation.
Drawing on literature regarding representation learning, studies suggest that
one key characteristic of good latent representations is the ability to produce
semantically mixed outputs when decoding linear interpolations of two latent
representations. We propose the Mixing Consistent Deep Clustering method which
encourages interpolations to appear realistic while adding the constraint that
interpolations of two data points must look like one of the two inputs. By
applying this training method to various clustering (non-)specific autoencoder
models we found that using the proposed training method systematically changed
the structure of learned representations of a model and it improved clustering
performance for the tested ACAI, IDEC, and VAE models on the MNIST, SVHN, and
CIFAR-10 datasets. These outcomes have practical implications for numerous
real-world clustering tasks, as it shows that the proposed method can be added
to existing autoencoders to further improve clustering performance.
