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Unsupervised learning Unsupervised Learning is implemented without any defined output for a given input set.The pattern or rule used for classification is learned by the algorithm itself while training.The task here is to generate a hypothesis for the input data and then obtain the output as per the postulates.In this process, all possible hypotheses are evaluated however the output is obtained using the optimal one of them all.

The method uses two different datasets that are passed on to the learning algorithm [18,22,23].Learning process is repeated until the error measure becomes sufficiently small or the process meets a failure [17,18].Gradient descent algorithm is used to minimise the error measure [19-21].Artificial Intelligence (AI) or Machine learning in present era serves as the primary choice for data mining and big data analysis.With effective learning and adaptation model, it provides solutions to several engineering applications.

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