Unsupervised learning is a class of ML where we do not have any clue about the target data. We have to approach the problem by clustering the given unlabeled data based on the relationship between the input variables.
We have to analyze the data; find patterns and relationships among them. Then based on that, group the training data into different clusters. Data in the same cluster are similar in some sense. These algorithms are mainly used in pattern detection and descriptive modeling.
The algorithms used in unsupervised learning are different from that of supervised learning. These algorithms are useful in cases where the human expert doesn’t know what to look for in the data. The unsupervised learning can be mainly classified into two groups:<br>
<br>There are different types of clustering: