  Clustering algorithms have significantly improved along with Deep Neural
Networks which provide effective representation of data. Existing methods are
built upon deep autoencoder and self-training process that leverages the
distribution of cluster assignments of samples. However, as the fundamental
objective of the autoencoder is focused on efficient data reconstruction, the
learnt space may be sub-optimal for clustering. Moreover, it requires highly
effective codes (i.e., representation) of data, otherwise the initial cluster
centers often cause stability issues during self-training. Many
state-of-the-art clustering algorithms use convolution operation to extract
efficient codes but their applications are limited to image data. In this
regard, we propose an end-to-end deep clustering algorithm, i.e., Very Compact
Clusters (VCC). VCC takes advantage of distributions of local relationships of
samples near the boundary of clusters, so that they can be properly separated
and pulled to cluster centers to form compact clusters. Experimental results on
various datasets illustrate that our proposed approach achieves competitive
clustering performance against most of the state-of-the-art clustering methods
for both image and non-image data, and its results can be easily qualitatively
seen in the learnt low-dimensional space.
