There are a lot of machine learning algorithms out there that can do a wide variety of tasks. You might know a lot about machine learning and human supervision that is involved in machine learning jobs.
A machine learning algorithm can be supervised or unsupervised, depending on the situation. Today, let’s look at some of the practical applications of unsupervised learning.
Unsupervised learning is more challenging than other strategies due to the absence of labels. However, they are very significant in machine learning since they can do very complex tasks efficiently.
Unsupervised learning has several real-world applications. Let’s see what they are.
The main applications of unsupervised learning include clustering, visualization, dimensionality reduction, finding association rules, and anomaly detection.
Let’s discuss these applications in detail.
Clustering is the process of grouping the given data into different clusters or groups. Unsupervised learning can be used to do clustering when we don’t know exactly the information about the clusters.
Elements in a group or cluster should be as similar as possible, and points in different groups should be as dissimilar as possible.
Unsupervised learning can be used to do clustering when we don’t know exactly the information about the clusters.
It is used for analyzing and grouping data, which does not include pre-labeled classes or class attributes. Clustering can be helpful for businesses to manage their data in a better way.
For example, you can go to Walmart or a supermarket and see how different items are grouped and arranged there.
Also, e-commerce websites like Amazon use clustering algorithms to implement a user-specific recommendation system.
Here is another example. Let’s say you have a YouTube channel. You may have a lot of data about the subscribers of your channel. If you want to detect groups of similar subscribers, then you may need to run a clustering algorithm.
You don’t need to tell the algorithm which group a subscriber belongs to. The algorithm can find those connections without your help.
For example, it may tell you that 35% of your subscribers are from Canada, while 20% of them are from the United States.
Similarly, it can give a lot of information, and this will help you to target your videos for each group. You can use a hierarchical clustering algorithm to subdivide each group into smaller groups.
That is how clustering works with unsupervised machine learning. A lot of advanced things can be achieved using this strategy.
In unsupervised learning, we have some data that has no labels. We don’t really know anything about the data other than the features. There is no information about the class to which this data belongs.
So, we use clustering algorithms to find out these clusters and their classes.
These are some of the commonly used clustering algorithms:
- Expectation Maximization
- Hierarchical Cluster Analysis (HCA)
Now, let’s look at another application of unsupervised learning, which is visualization.
Visualization is the process of creating diagrams, images, graphs, charts, etc., to communicate some information. This method can be applied using unsupervised machine learning.
For example, let’s say you are a football coach, and you have some data about your team’s performance in a tournament. You may want to find all the statistics about the matches quickly.
You can feed the complex and unlabeled data to some visualization algorithm.
These algorithms will output a two-dimensional or three-dimensional representation of your data that can easily be plotted. So, by seeing the plotted graphs, you can easily get a lot of information.
This information will help you to maintain your winning formula, correct your previous mistakes, and win the ultimate trophy.
One example of a visualization algorithm is t-distributed Stochastic Neighbor Embedding (t-SNE).
If you want to learn data visualization, I’ve written a beginner’s guide on Data Visualization using Matplotlib. Do check it out.
Now, let’s continue to the next application of unsupervised learning, which is 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.
It is always a good practice to try to reduce the dimensionality of your training data using an algorithm before you feed the data to another machine learning algorithm.
This will make the data less complex, much faster, the data may take up less memory, and it will perform better at some times.
Reducing the dimensionality may lose some information. So, even if this will speed up the training, most of the time, it may also make your system perform slightly worse.
So, use dimensionality reduction only if the training is too slow. Otherwise, try to use the original data.
These are some of the most common dimensionality reduction algorithms in machine learning:
- Principal Component Analysis (PCA)
- Kernel PCA
- Locally-Linear Embedding
Now, let’s look at the next application of unsupervised learning, which is finding association rules.
Finding Association Rules
This is the process of finding associations between different parameters in the available data. It discovers the probability of the co-occurrence of items in a collection, such as people that buy X also tend to buy Y.
In association rule learning, the algorithm will deep dive into large amounts of data and find some interesting relationships between attributes.
For example, when you go to Amazon and buy some items, they will show you products similar to those in advertisements, even when you are not on their website.
This is a kind of association rule learning. Amazon can find associations between different products and customers. They know that if they show a particular advertisement to a particular customer, chances are high that he will buy the product.
Thus, by using this method, they can increase their sales and revenue very highly. This leads to a more customized customer approach and is a pillar to customer satisfaction as well as retention.
These are some of the commonly used algorithms for association rule learning:
Now, let’s look at another important application of unsupervised learning, which is, anomaly detection.
Anomaly detection is the identification of rare items, events, or observations, which brings suspicions by differing significantly from the normal data.
In this case, the system is trained with a lot of normal instances. So, when it sees an unusual instance, it can detect whether it is an anomaly or not.
One important example of this is credit card fraud detection. You might have heard about a lot of events related to credit card fraud.
This problem is now solved using anomaly detection techniques in machine learning. The system detects unusual credit card transactions to prevent fraud.
We’ve discussed the 5 different categories of unsupervised learning applications. Now, let’s learn some more essential things related to unsupervised learning.
More on Unsupervised Learning
Unsupervised learning has way more applications than most people think. Despite its comparatively little use in industry, it’s the most effective method for discovering inherent patterns in data that otherwise wouldn’t be obvious.
We mostly hear of supervised learning, but unsupervised learning is playing a huge role in many real-world needs of human beings.
Unsupervised Learning is the subset of machine learning that helps when you have a dataset though you don’t know the output value. In the unsupervised machine learning approach, you only have input data and no corresponding output variables.
Unsupervised learning has several advantages as well as disadvantages. Check out this article to find out the pros and cons of unsupervised learning.
Supervised vs Unsupervised vs Reinforcement Learning
Generally, there are four types of machine learning strategies out there that we can use to train the machine: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
In supervised learning, some labels are also associated with the training. Unsupervised learning does not contain any labels.
Semisupervised learning is a mixture of supervised learning and unsupervised learning. These algorithms deal with partially labeled data.
In reinforcement machine learning, the machine learns by itself after making many mistakes and correcting them.
Out of these four, which one is the best machine learning strategy? The answer is, it depends on what your goal exactly is. There are various types of algorithms available under all these four strategies.
Each algorithm has its own purpose. Some algorithms are suitable for anomaly detection. Clustering will be the application of some others. Some of the algorithms may be perfect for visualization, finding associations, predicting numerical results, etc.
All these algorithms perform differently for different applications, and we need to choose the right algorithm for the right type of application.
If you are a beginner in machine learning and don’t know the basics, I suggest you check out this article. If you want to become a machine learning expert by learning things in the right way, I recommend you read this article.
If you have any doubts regarding machine learning and deep learning, feel free to ask them in the comments section.
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6 thoughts on “Real-world Applications of Unsupervised Learning”
Hi Ashwin, comprehensive and clear article on Unsupervised learning. Thanks!
can you please suggest if unsupervised learning is preferred to detect if the news is negative news or positive news and why
Yes, unsupervised learning is the best option for that since there will be a lack of labeling. Hence, the machine needs to do the classification by itself.
This is an amazing article! Helped a lot!