Suppose you had a basket and it is filled with some fresh fruits your task is to arrange the same type of fruits at one place. Suppose the fruits are apple, banana, cherry, grape.
So you already know from your ‘previous work’ that, the shape of each and every fruit so it is easy to arrange the same type of fruits at one place. Here your ‘previous work’ is called as ‘train data’ in data mining. So you already learn the things from your train data, This is because of you have a response variable which says you that if some fruit have so and so features it is grape, like that for each and every fruit.
This type of data you will get from the train data. This type of learning is called as supervised learning. So you already learn the things and can do you job confidently.
Unsupervised Learning :
Suppose you had a basket and it is filled with some fresh fruits your task is to arrange the same type fruits at one place. This time you don’t know any thing about that fruits, you are first time seeing these fruits so how will you arrange the same type of fruits.
What you will do first you take on fruit and you will select any physical character of that particular fruit. Suppose you take color. Then you will arrange them based on the color, then the groups will be some thing like this.
RED COLOR GROUP: apples & cherry fruits.
GREEN COLOR GROUP: bananas & grapes.
so now you will take another physical character as size, so now the groups will be some thing like this.
RED COLOR AND BIG SIZE: apple.
RED COLOR AND SMALL SIZE: cherry fruits.
GREEN COLOR AND BIG SIZE: bananas.
GREEN COLOR AND SMALL SIZE: grapes.
job done happy ending.
Here you didn’t know learn any thing before means no train data and noresponse variable.
This type of learning is know unsupervised learning.
Classification and regression are types of supervised learning while, clustering is unsupervised learning.