Clustering and classification are machine learning methods for finding the similarities – and differences – in a set of data or documents. These methods can be used for such tasks as grouping products in a product catalog, finding cohorts of similar customers, or aggregating sets of documents by topic, team, or office.
Classifications take a set of data that you’ve already manually analyzed and labeled and uses that to train a learning model to then examine a set of new data. This is called supervised learning.
Clustering on the other hand, doesn’t require an existing data set that’s been labeled by humans but still tries to find the groupings and differences in the data. This is called unsupervised learning.