  In this paper, we propose a methodology to improvise the technique of deep
transfer clustering (DTC) when applied to the less variant data distribution.
Clustering can be considered as the most important unsupervised learning
problem. A simple definition of clustering can be stated as "the process of
organizing objects into groups, whose members are similar in some way". Image
clustering is a crucial but challenging task in the domain machine learning and
computer vision. We have discussed the clustering of the data collection where
the data is less variant. We have discussed the improvement by using
attention-based classifiers rather than regular classifiers as the initial
feature extractors in the deep transfer clustering. We have enforced the model
to learn only the required region of interest in the images to get the
differentiable and robust features that do not take into account the
background. This paper is the improvement of the existing deep transfer
clustering for less variant data distribution.
