What Are the Benefits of Unsupervised Machine Learning and Clustering?

What Is Unsupervised Learning?

Unsupervised learning is a type of machine learning that uses algorithms to analyze and draw inferences from unlabeled data.

The model is not given explicit instructions on what to look for but instead is tasked with identifying patterns, structures and anomalies on its own.

“Unsupervised learning is helpful when you don’t have labels for data but still want to understand the structure and patterns within it,” says Forrester Vice President and Principal Analyst Brandon Purcell. “It allows companies to discover segments, anomalies and relationships without any human guidance.”

The most typical applications include customer segmentation, market basket analysis, recommendation systems and anomaly detection.

These models are often the first step in exploring new data sets, enabling data scientists and analysts to better understand the makeup and inner workings of complex information.

What Is Unsupervised Clustering?

Unsupervised clustering is one of the most widely used techniques within unsupervised learning. The goal is to divide a data set into groups (or clusters) based on similarities, so that items within the same cluster are more alike.

“The algorithm doesn’t know in advance what the right groups are,” Purcell says. “It uses distance or similarity metrics to group data points together.”

According to Purcell, this is especially valuable when you’re trying to understand your customers but you don’t have labels that define their behaviors.

Clustering can be done using various algorithms such as k-means, hierarchical clustering, density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM) clustering.

Each approach has its benefits depending on the shape, size and distribution of the data.

How Does Unsupervised Learning and Clustering Work?

Unsupervised learning starts by feeding a large, unlabeled data set into an algorithm that identifies patterns or groupings using statistical methods to find related data points.

Clustering then calculates the “distance” between data points in a multidimensional space and groups the most similar ones together. It creates a feature space based on variables such as user behavior, product preferences and sensor readings. The algorithm then looks for clusters of data points that naturally form based on these features.

“In most cases, the goal isn’t to build a predictive model right away,” Purcell says. “It’s about exploring the data, finding new ways to categorize or structure it and using that knowledge to inform further analysis.”

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What Are Some Industry Examples of Unsupervised Learning?

Clustering analysis and other forms of unsupervised learning are used across industries, including healthcare, finance, retail and manufacturing.

In healthcare, clustering helps identify patient populations with similar health characteristics or treatment responses.

“You can use clustering to group patients based on historical data and then design interventions tailored to each group,” Purcell says.

In financial services, it’s used to detect fraud by identifying abnormal patterns that deviate from the norm (an application known as anomaly detection).

“Unsupervised learning is particularly useful in identifying activities that don’t fit existing rules but may still be suspicious,” Purcell adds.

In retail and e-commerce, clustering enables more effective customer segmentation by analyzing purchase behavior, browsing history and demographic data.

“Retailers can create microsegments of customers and personalize marketing strategies,” he says.

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How Do Clustering Insights Help Organizations?

The core value of unsupervised learning and clustering lies in the ability to reveal insights that may otherwise remain hidden, because too often, enterprises are struggling to “figure out what to do with the large volumes of data they have,” Purcell says.

Once the data is organized, IT leaders can use it for predictive analytics and business intelligence. For example, a company discovering that a particular customer segment is much more price-sensitive than other segments might offer targeted discounts only to that group, improving marketing ROI.

Unsupervised machine learning can also help build robust AI models, especially when labeled data is scarce or expensive to obtain.

Purcell says these capabilities are particularly important at a time when agility, adaptability and insight are key to staying competitive.

“Organizations sit on mountains of data, much of it unlabeled and unused,” says Purcell.  “Clustering analysis helps make that data useful by organizing it into understandable groups.”