Machine Learning Unsupervised : 3 Main Tasks

Hrvoje Smolic
Co-Founder, CEO, Graphite Note

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Are you just now stumbling onto the term machine learning: unsupervised learning?

This fascinating technology leaves an Machine Learning algorithms to learn on their own using plain, unlabeled examples - often revealing interesting insights in the process.

Typically used by analysts to find hidden patterns in data sets, its beauty lies in the fact that it needs no human intervention. That means you won't need to sit down and assist in its processes.

But how exactly does unsupervised learning benefit you or impact your decisions? Dive into the world of unsupervised machine learning to find out more.

Machine Learning Unsupervised - What Is It?

Also known as unsupervised learning, it uses machine learning to categorize and analyze unlabeled data. Often used in exploratory data analysis, its wide range of applications endear it to many business owners and strategists.

Unlike supervised machine learning, you're working blindly without clear values. As such, it can't be applied to most machine learning approaches that require specific data values - or labeled datasets.

However, while it can't be applied directly to problems that require regression and classification results, it's quite helpful in helping you discover the structure of your data. This makes it useful across a wide range of other data science applications - ranging from customer analytics to understanding whale language.

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Photo by Pawel Czerwinski on Unsplash

How Do Machines Learn In This Setup?

Unsupervised machine learning models use input data only to learn. It then applies specific algorithms to automatically analyze the data sets. After that, the data is segmented into groups.

Its main goal is to figure out relationships within the dataset it's fed. As such, it's more frequently used to gather results when you don't necessarily know what to expect.

Put simply, unsupervised learning uses input data to find the value of output data.

The Importance Of Unsupervised Machine Learning

Unsupervised machine learning is more commonly used to help you understand your existing customer base on a deeper level. Since there is no way to measure the accuracy of its results, unsupervised machine learning shouldn't be used to analyze data where you have an expected output.

Despite its shortcomings, unsupervised machine learning is still a powerful data analysis tool that can help you find unknown patterns. Because of this, it's also often used in cybersecurity to help determine hacking patterns.

Common Approaches In Unsupervised Machine Learning

Unsupervised learning models are often used to accomplish three main tasks. Depending on your needs, it's important to know which approach might work for you. Take a look at these approaches below.


The most common approach, clustering groups input-only data based on similarities and differences. This is helpful for finding specific patterns in the information you provide the model with - such as customer activity.

machine learning unsupervised
Image by the author: Clusters in Graphite Note

There are currently four sub-approaches when it comes to clustering.

  • Exclusive clustering asserts that data points can only appear in one group - hence the name. This is often used in market, image, and document segmentation.
  • Unlike the former, overlapping clusters allows data points to belong in multiple clusters.
  • Hierarchical clustering, commonly known as HCA, is used to categorize data sets based on their similarities following a hierarchical structure. Often used to organize social network data, it’s similar to the way files on your computer are segmented into folders.
  • Probabilistic clustering is used to solve soft clustering problems. Unlike the former approaches, this method groups data points according to how likely they are to belong to certain distributions. One of its most common examples is the Gaussian Mixture Model.

Association Rules

This method follows a specific set of rules to determine relationships between data points. Usually, it’s used in market basket analytics where businesses analyze customer activity based on specific patterns.

You’ll see these most often applied in cross-selling and upselling tactics or recommendation engines like those you see on marketplaces such as Amazon. If you’ve ever seen a “What’s trending today” section on your browser - this is most likely the fruit of the association rules approach.

Dimensionality Reduction

This approach is used to prevent overfitting. When your data set has a high value density, dimensionality reduction minimizes the data inputs into smaller, bite-sized pieces. What’s even more impressive is that it does so without harming the integrity of your data.

There are several dimensionality reduction methods used to preprocess data such as:

  • Principal component analysis or PCA
  • Singular value decomposition or SVD
  • Autoencoders
Machine Learning Unsupervised - clustering
Image by the author: Cluster visualization in Graphite Note

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What Can Machine Learning Unsupervised Be Used For?

Unsupervised learning is mainly used to help improve user or customer experience. Beyond that, it also has applications in cybersecurity, social networking, and quality assurance for systems.

In its purest sense, UML can give you a glimpse of large data sets to help you cultivate a result. Take a look at its most common applications below:


Marketplaces and webstores often apply “customer who bought this also bought” techniques. This not only drives interest in certain products, but it also helps upsell customers in a way that looks like you’re providing added value to their experience.


Google News is one of the best examples when it comes to unsupervised learning. The platform categorizes its articles into sections labeled under specific themes to make it easier for their readers to find relevant information.

Computer Vision

Object recognition is one of the most common examples of applied unsupervised machine learning. These perception tasks help computers index information for recognizing objects like when you want your camera to autofocus on a subject.

Detecting Anomalies

While unsupervised learning can be used to find similarities in a data set, it’s also efficient in finding new activity. Commonly applied in cybersecurity, anomalies alert the analyst when there is a potential threat in security, or when questionable activities happen within your servers.


Unsupervised machine learning may not be able to provide you with specific values, but it’s highly effective when it comes to giving you answers. From identifying your customer activities to creating pitch-perfect recommendations, this machine learning method shouldn’t be ignored.

Platforms like Graphite employ the use of unsupervised machine learning to help you segment data points more accurately. And the best part is that you don’t even need to learn a single line of code!

If you want to leverage the full potential of unsupervised machine learning, then it’s a good idea to get in touch with industry experts as soon as possible.

🤔 Want to see how Graphite Note works for your AI use case? Book a demo with our product specialist!

You can explore all Graphite Models here. This page may be helpful if you are interested in different machine learning use cases. Feel free to try for free and train your machine learning model on any dataset without writing code.


This blog post provides insights based on the current research and understanding of AI, machine learning and predictive analytics applications for companies.  Businesses should use this information as a guide and seek professional advice when developing and implementing new strategies.


At Graphite Note, we are committed to providing our readers with accurate and up-to-date information. Our content is regularly reviewed and updated to reflect the latest advancements in the field of predictive analytics and AI.

Author Bio

Hrvoje Smolic, is the accomplished Founder and CEO of Graphite Note. He holds a Master's degree in Physics from the University of Zagreb. In 2010 Hrvoje founded Qualia, a company that created BusinessQ, an innovative SaaS data visualization software utilized by over 15,000 companies worldwide. Continuing his entrepreneurial journey, Hrvoje founded Graphite Note in 2020, a visionary company that seeks to redefine the business intelligence landscape by seamlessly integrating data analytics, predictive analytics algorithms, and effective human communication.

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