In this post, we will understand the difference between classification and clustering.
It is used with supervised learning.
It is a process where the input instances are classified based on their respective class labels.
It has labels hence there is a need to train and test the dataset to verify the model.
It is more complex in comparison to clustering.
Examples: Logistic regression, Naive Bayes classifier, Support vector machines.
It is used with unsupervised learning.
It groups the instances based on how similar they are, without using class labels.
It is not needed to train and test the dataset.
It is less complex in comparison to classification.
Examples: k-means clustering algorithm, Gaussian (EM) clustering algorithm.