It might be confusing for beginners to distinguish between a clustering problem and a classification problem. Classification is fundamentally different from clustering. Classification is a supervised learning problem where your class or target variable is known to train a dataset. The algorithm is trained to look at the examples (features and class or target variables) and then you score and test it with a test dataset.

Clustering, being an unsupervised learning, it works on a dataset with no label or class variable. Also, you don't perform scoring and testing with a test dataset. So, you just apply your algorithm to your data and group them into a different cluster, say 1, 2 and 3, which were not known before.

So, to put it simply, if you have a dataset and a class/label or target variable as categorical variable and you have to predict the target variable for a new dataset based on the given dataset, it's a classification problem. If you are just given a...