Clustering is often considered a classic example of unsupervised learning. It is a method of dividing the dataset into multiple groups where the objects in the same group will be more similar to each other than those in the other groups.
Clustering algorithms are generally used on unlabeled datasets; hence, there is no way to measure the clustering output. The user, based on his requirement, should consider the variables carefully so that the resultant clusters closely match with the user's requirement.
The greatest example for the clustering algorithms would be a search engine where the pages that are closely related to each other are shown together and the pages that are different are kept apart as far as possible. The most important factor here is to measure the similarity or dissimilarity between the objects.
Some of the problems that can be solved through the implementation of clustering algorithms are the predicting of a disease in the medical...