Applications of unsupervised learning in multiple domains

Unsupervised Learning helps in a variety of ways which can be used to solve various real-world problems.

• They help us in understanding patterns which can be used to cluster the data points based on various features.
• Understanding various defects in the dataset which we would not be able to detect initially.
• They help in mapping the various items based on the dependencies of each other.
• Cleansing the datasets by removing features which are not really required for the machine to learn from.

This ultimately leads to applications which are helpful to us. Certain examples of where Unsupervised Learning algorithms are used are discussed below:

• Amazon: Amazon also uses unsupervised learning to learn the customer’s purchase and recommend the products which are most frequently bought together which is an example of association rule mining.
• AirBnB: This is a great application which helps host stays and experiences connecting people all over the world. This application uses Unsupervised Learning where the user queries his or her requirements and Airbnb learns these patterns and recommends stays and experiences which fall under the same group or cluster.
• Credit-Card Fraud Detection: Unsupervised Learning algorithms learn about various patterns of the user and their usage of the credit card. If the card is used in parts that do not match the behaviour, an alarm is generated which could possibly be marked fraud and calls are given to you to confirm whether it was you using the card or not.
• Dimensionality Reduction: Dimensionality reduction is the process of reducing the number of random variables under consideration by getting a set of principal variables.

Many machine learning problems contain thousands of features for each training instance. This will make the training slow, and it will be difficult to obtain a proper solution to the problem.

In dimensionality reduction, the objective is to simplify the data without losing too much information. There can be a lot of similar information in your data. One method to do dimensionality reduction is to merge all those correlated features into one. This method is also called feature extraction.