|Classification is supervised learning technique used to assign per-defined tag to instance on the basis of features||Clustering is unsupervised technique used to group similar instances on the basis of features.|
|Classification algorithm requires training data.||Clustering does not require training data.|
|Classification,model is uses pre-defined instances.||Clustering does not assign pre-defined label to each and every group.|
|With classification the groups (or classes) are specified before hand, with each training data instance belonging to a particular class.||With clustering the groups (or clusters) are based on the similarities of data instances to each other.|
|Classification algorithms are supposed to learn the association between the features of the instance and the class they belong to.||No predefined output class is used in training and the clustering algorithm is supposed to learn the grouping.|
|Example : An insurance company trying to assign customers into high risk and low risk categories.||Example : An online movie company recommending you a movie because other customers who had made similar movie choices as you in the past have favorably rated that movie.|
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