  In this paper, we propose a strategy to mitigate the problem of inefficient
clustering performance by introducing data augmentation as an auxiliary
plug-in. Classical clustering techniques such as K-means, Gaussian mixture
model and spectral clustering are central to many data-driven applications.
However, recently unsupervised simultaneous feature learning and clustering
using neural networks also known as Deep Embedded Clustering (DEC) has gained
prominence. Pioneering works on deep feature clustering focus on defining
relevant clustering loss function and choosing the right neural network for
extracting features. A central problem in all these cases is data sparsity
accompanied by high intra-class and low inter-class variance, which
subsequently leads to poor clustering performance and erroneous candidate
assignments. Towards this, we employ data augmentation techniques to improve
the density of the clusters, thus improving the overall performance. We train a
variant of Convolutional Autoencoder (CAE) with augmented data to construct the
initial feature space as a novel model for deep clustering. We demonstrate the
results of proposed strategy on crowdsourced Indian Heritage dataset. Extensive
experiments show consistent improvements over existing works.
