How Unsupervised Learning Works with Examples

Machine learning is a powerful technology that has revolutionized the way we live and work. Unsupervised learning techniques can help uncover patterns and insights in large and complex data sets, making it a valuable skill across many industries. By understanding how unsupervised learning works and its characteristics, you can learn to use its features for different functions and enhance your professional skill set.

Supervised vs. unsupervised machine learning

When you design a machine learning algorithm, you can choose between supervised and unsupervised techniques. With supervised learning, you train the machine learning model by providing labeled input and output variables to the algorithm. With these labeled inputs and outputs, the algorithm can make predictions and then adjust for the actual output value. This helps the algorithm learn how the variables relate to one another, which can later inform predictions with new data points.

In contrast, unsupervised learning deals with unlabeled data, where you do not give the algorithm any specific output to predict. Instead, unsupervised learning reveals hidden patterns or structures in the data without human guidance or intervention. It does this through different techniques, including clustering and dimensionality reduction of the data set.

How unsupervised learning works 

When using unsupervised learning, the goal is for your algorithm to uncover patterns and structures in a data set without your guidance beforehand. Essentially, you give the algorithm a data set, and the algorithm must identify any inherent relationships, similarities, or differences between the data points.

The algorithm will typically begin by analyzing the data to identify any existing commonalities or patterns. For example, the algorithm may start doing this by grouping similar data points based on specific attributes or features. This process continues iteratively, and the algorithm will keep refining this process until it identifies a set of groups that accurately represent the underlying structure of the data. Because you don’t provide the algorithm with an intended outcome or type of insight, this type of machine learning can be very powerful for uncovering hidden patterns or associations you hadn’t initially considered.

Unsupervised learning examples

Unsupervised learning is versatile in its applications, making it an excellent tool for professionals across many industries. If you are considering ways in which you might benefit from learning skills in this area, consider the following examples:

Categorizing news feeds 

You can use unsupervised learning to categorize news feeds based on their content. This type of algorithm can identify patterns in the text and group similar articles together. You may notice many news websites use this type of algorithm, many of which use categorization techniques to make it easier for users to find articles on specific topics. 

Another way you can use this type of classification is to personalize news feeds for users. For example, Facebook uses a machine learning algorithm to filter news articles and categorize them to show the most relevant ones for different users based on their activity.

Medical diagnostics

As a health care provider, you could benefit from unsupervised learning to validate your diagnoses and help identify at-risk patients earlier. Unsupervised learning techniques can identify patterns in patient data that may indicate a disease or condition. For example, you could provide your unsupervised learning algorithm with a large number of patient medical records for patients with the same condition. The algorithm could then identify commonalities between patients that health care professionals missed previously and help to improve diagnostic and screening criteria in the future.

Retail recommendations

If you work in e-commerce, you can use unsupervised learning to recommend products to customers. Unsupervised learning algorithms can analyze customer purchase histories and identify patterns in the types of products they buy. Based on those patterns, the algorithm can recommend new products that the customer is likely to enjoy and increase conversion rates for the company.

Types of unsupervised learning algorithms 

When designing unsupervised learning algorithms, you will likely use three main approaches: clustering, association rule learning (ARL), and dimensionality reduction. Clustering groups data points together based on their relationship to each other. You can do this by using various algorithms, such as K-means, hierarchical clustering, or DBSCAN.

ARL looks for associations between data points after clustering. This learning algorithm is highly scalable and useful for user insights. For example, it may identify that users buying a new lamp are also likely to buy a new light bulb.

On the other hand, dimensionality reduction involves minimizing the features in the data set while maintaining as much data integrity as possible. You might do this by combining heavily correlated variables into a single variable. For example, if you had a data set with Chihuahuas and Great Danes and labeled every Chihuahua as “small” and every Great Dane as “big,” you might combine the “small” and “Chihuahua” variables and the “big” and “Great Dane” variables. It can help visualize complex data sets or identify important features that may be driving patterns in the data.

Types of clustering 

Depending on the application for the program you are developing, you might use several subtypes of clustering algorithms. Common examples include:

Exclusive clustering

Exclusive clustering, or partitioning clustering, involves dividing data points into non-overlapping clusters. The algorithm determines the cluster to which each data point belongs based on its similarity to other data points in that cluster.

Overlapping clustering

Overlapping clustering involves allowing data points to belong to groups of two or more clusters. It can be useful when data points have characteristics that could place them in several clusters.

Hierarchical clustering 

Hierarchical clustering involves organizing data points into a tree-like structure with similar data points grouped together. The algorithm can be either agglomerative, where each data point starts as its own cluster and gradually merges with other clusters, or divisive, where all data points begin in a single cluster and gradually divide into smaller clusters.

Probabilistic clustering 

Probabilistic clustering involves assigning each data point a probability of being included in each cluster. It can be helpful when data points have attributes you can’t easily classify into discrete categories.

Benefits of unsupervised learning

Whether you are well-versed in machine learning or just getting started, utilizing unsupervised learning techniques may offer benefits such as:

• Identifying trends and connections: Unsupervised learning algorithms can spot links and patterns that conventional statistical methods may miss. 

• Minimizing human bias: Unsupervised learning algorithms can lessen bias in data analysis because they require less human direction. Instead of telling the algorithm what variables are associated, the algorithm identifies associations independently.

• Reducing analysis time: Unsupervised learning algorithms can identify patterns and trends in large data sets faster than humans.

• Detecting outliers: Unsupervised learning algorithms can spot outliers or anomalies in data, which can help find fraudulent activity or data errors.

• Predicting future trends: Unsupervised learning algorithms can evaluate time series data, such as weather patterns, to spot trends or forecast new ones.

Challenges for unsupervised learning 

Understanding the challenges associated with unsupervised learning can help you determine which applications will be most beneficial for you. While challenges will vary depending on your intended uses, some potential challenges include:

• Difficulty evaluating results: Since data is not labeled before analysis, it can be challenging to assess whether the results accurately describe the true structure of the data.

• Higher costs: Because the algorithm is more complex, this type of analysis can take longer than other methods and be more resource-intensive, especially when needing to validate the results externally.

• Lack of transparency: Because you are minimally involved in the computations and clustering, there may be less clarity on how the algorithm determines specific associations and inferences.

How to start learning unsupervised learning

You can choose from many resources and approaches when learning unsupervised learning techniques. Here are some steps you can follow to start learning:

1. Learn the basics of machine learning: Before diving into unsupervised learning, you should have a solid understanding of the basics of machine learning. This will help you understand when and why you should use unsupervised learning techniques. Completing the Machine Learning Specialization on Coursera is a great way to build in-depth knowledge.

2. Choose a programming language: You can choose between several programming languages commonly used for machine learning, including Python and R. Building skills in one of these languages can help you feel comfortable learning basic machine language operations.

3. Explore unsupervised learning concepts: Once you build a basic understanding of machine learning and programming, the next step is learning unsupervised learning techniques. You can choose from several beginner courses on Coursera, including Unsupervised Machine Learning by IBM.

4. Practice with data sets: To understand unsupervised learning, you must practice using it on real data sets. You can find many data sets online that you can use for practice, including the UCI Machine Learning Repository.

5. Join a community: Joining a community of machine learning enthusiasts can be a great way to get support and learn from others. Consider joining online forums, such as the Kaggle community.

Continue learning with Coursera.

Unsupervised learning is a powerful machine learning technique used to find underlying patterns and trends in complex data sets. As a professional, you can use unsupervised learning to segment customers, predict trends, diagnose diseases, and more. 

Becoming comfortable with machine learning techniques, such as unsupervised learning, can help expand your job opportunities and open doors for career opportunities in new fields. To focus on unsupervised learning, consider taking the Machine Learning Specialization, which includes the Unsupervised Learning, Recommenders, Reinforcement Learning course, offered by DeepLearning.AI and Stanford University on Coursera.