Both Classification and Clustering are utilised for the categorization of objects and to analyse the collected data on the basis of features. Let’s discuss some major differences between classification and clustering.
What is Classification?
Classification is a kind of supervised machine learning algorithm used to label data. It helps in the projection of the class of the output variable. There are basically two types of classifications:
- Binary Classification: When we have to organise data into two classes.
- Multiclass Classification: When we have to sort or classify the data into more than two classes.
What is Clustering?
Clustering is a kind of unsupervised learning algorithm. Clustering is the collection of objects based on resemblance and distinction between them. In simple words, we can say that it is an approach of collection of objects, so that objects with similar functionalities come together and objects with different attributes move apart.
Difference between Classification and Clustering
| S.No. | Classification | Clustering |
| 1 | It is an approach to classifying the input instances on the basis of related class labels. | It is used to set the instances on the basis of their resemblance without class labels. |
| 2 | Classification is a type of supervised learning method. | Clustering is a kind of unsupervised learning method. |
| 3 | It prefers a training dataset. | It does not prefer a training dataset. |
| 4 | Classification is more complex as compared to clustering. | Clustering is less complex as compared to the classification. |
| 5 | Here, we utilised the labels for training data. | Here, we don’t prefer the labels for training data. |
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