Unsupervised learning is based on problems where there is no supervisor to explain to you about the data unlike supervised learning, i.e. when the data is not labelled. When the problems are based on how you can group items based on certain similarities, then you should use the unsupervised machine learning approach. In this article, I will introduce you to the applications of unsupervised learning.
What is Unsupervised Learning?
Unsupervised learning is based on problems where you do not get any information about the output values. Since the data is not labelled, this approach is useful when there is a need to learn how a set of items can be grouped based on the similarities between them. Unsupervised learning provides an implicit descriptive analysis of the pieces of information uncovered by any clustering algorithm that can be used to obtain complete information from an unlabeled dataset.
In unsupervised learning, we aim to extend the characteristics of certain data points to their neighbours by assuming that the similarities between them are not limited to some specific features only. For example, in a recommendation system, a group of users can be grouped based on their interests in certain movies. If the chosen criteria detected analogies between the 2 users, we can share the non-overlapping elements between the users.
Applications of Unsupervised Learning
Hope you now understand what unsupervised learning is in machine learning. In short, it means finding similarities between an unlabeled dataset. Some of the common applications where unsupervised learning is used are:
- Products Segmentation
- Customer Segmentation
- Similarity Detection
- Recommendation Systems
- Labelling unlabelled datasets
So unsupervised learning is used in the problems where you have to find similarities in an unlabelled dataset. I hope you liked this article on the applications of unsupervised learning. Feel free to ask your valuable questions in the comments section below.