What's the difference between data classification and clustering (from a Data point of view)
Classification is a problem where your input data consists of elements with 2 parts:

Some data features that reflect the traits of an entity
A label that assigns the entity to a group or class.

With that kind of data, you can train a model that receives the data features (first part) and generates the label (second part). This kind of training, where you train a system to generate some output when it receives a specific input is called &quot;supervised learning&quot;.
On the other hand, in Clustering, your dataset only has the data features, that is, your dataset does not have the labels. Clustering methods allow you to group the entities in classes without having any labels, normally by defining a priori how many groups you want, and then grouping the entities by their similarity. This kind of training, where there are no labels and you have to learn just from the entity data features is called &quot;unsupervised learning&quot;