Since the past few years research has been directed towards the training of neural networks
using unlabeled data or pairwise pseudo constraints known as unsupervised learning and
semi-supervised learning respectively. In this thesis, we explored several methods to improve the performance of the current state-of-the-art algorithm for unsupervised learning
called SCAN using semi-supervision from pairwise pseudo constraints.M.S
