Difference between Classification and Clustering in Machine Learning

	Classification	Clustering
What is it?	Given a set of historical/old data along with their class name and a set of new data, classification is the process of assigning each new data with class name that is obtained from the old/historical data.	Given a set of samples/data, clustering is the process of grouping the data based on their similarities and the patterns of the data.
Type of input data	Set of data with label/class name	Only the data set. No labeling is required.
Input	A set of samples or data A set of classes	A set of samples
Output	Class name of each new sample based on the classes of existing samples	Groupism of set of samples based on the data patterns.  Each sample will be assigned with a group/label/class name. Number of classes can be known.
Number of classes	Number of classes are known	Unknown number of classes.  (number of classes can be known after clustering process is over)
Learning type	Supervised learning	Unsupervised learning
Algorithms	Decision tress Bayesian classifier SVM	K-mean Expectation maximization
Dependencies	Depend on training data	no training data is required. No prior knowledge is required.

[src] : 

https://stackoverflow.com/questions/5064928/difference-between-classification-and-clustering-in-data-mining

http://www.differencebetween.net/technology/difference-between-clustering-and-classification/