The availability of labeled data is not very common and manually labeling data is also not cheap. This is the situation where unsupervised learning comes into play.

For example, one small boutique firm wants to roll out a promotion to its customers, who are registered on their Facebook page. While the business objective is clear—that a promotion needs to be rolled out to customers—it is unclear as to which customer falls under which group. Unlike the supervised learning method where prior knowledge existed in terms of bad debtors and good debtors, in this case there are no such clues.

When the customer information is given as input to unsupervised learning algorithms, it tries to identify the patterns in the data and thereby groups the data of the customers with similar kinds of attributes.

The reasoning behind the formation of these organic groups from the grouping exercise may not be very intuitive. It may take some research to identify the factors that contributed to the gathering of a set of customers in a group. Most of the time, this research is manual and the data points in each group need verifying. This research may form the basis to determine the groups to which the particular promotion at hand needs to be rolled out. This application of unsupervised learning is called clustering. The following diagram shows the application of unsupervised ML to cluster the data points:

There are a number of clustering algorithms. However, the most popular ones are namely, k-means clustering, k-modes clustering, hierarchical clustering, fuzzy clustering, and so on.

Other forms of unsupervised learning do exist. For example, in retail industry, an unsupervised learning method called association rule mining is applied on customer purchases to identify the goods that are purchased together. In this case, unlike supervised learning, there is no need for labels at all. The task involved only requires the ML algorithm to identify the latent associations between the products that are billed together by customers. Having the information from association rule mining helps retailers place the products that are bought together in proximity. The idea is that customers can be intuitively encouraged to buy the extra products.

A priori, equivalence class transformation (Eclat), and frequency pattern growth (FPG) are popular among the several algorithms that exist to perform association rule mining.

Yet another form of unsupervised learning is anomaly detection or outlier detection. The goal of the exercise is to identify data points that do not belong to the rest of the elements that are given as input to the unsupervised learning algorithm. Similar to association rule mining, due to the nature of the problem at hand, there is no requirement for labels to be made use of by the algorithm to achieve the goal.

Fraud detection is an important application of anomaly detection in the credit cards industry. Credit card transactions are monitored in real time and any spurious transaction patterns are flagged immediately to avoid losses to the credit card user as well as the credit card provider. The unusual pattern that is monitored for could be a huge transaction in a foreign currency rather than that of a normal currency in which the particular customer generally transacts. It could be transactions in physical stores located in two different continents on the same day. The general idea is to be able to flag up a pattern that is a deviation from the norm.

K-means clustering and one-class SVM are two well-known unsupervised ML algorithms that are used to observe abnormalities in the population.

Overall, it may be understood that unsupervised learning is unarguably a very important method, given that labeled data used for training is a scarce resource.