Glossary

What is Clustering?

Clustering is a method of segmenting a population into subgroups where members are more similar to each other than to members of other subgroups based on certain observed features. In machine learning, clustering is an unsupervised learning process, where categories are discovered and optimized to determine where new members belong.

Why is Clustering Important?

Clustering is a technique applied across many different use cases in business, healthcare, politics, and science. For example, clustering can group buyers with similar buying behavior in order to make more compelling recommendations for future purchases based on the cluster’s previous buying behaviors. Clustering can also be used in healthcare to predict how different subsets of a population may react to drug treatments.

How C3 AI Enables Organizations to Use Clustering

C3 AI makes it easy to apply clustering algorithms to address domain-specific applications of AI to deliver business value today. The C3 AI^® Suite is a complete, end-to-end platform for designing, developing, deploying, and operating enterprise AI applications at industrial scale. Both low-code C3 AI ML Studio and no-code C3 AI Ex Machina come with tools to tap into clustering algorithms and can be readily applied to new machine learning workflows, making it easy for users of all skill levels to generate valuable insights. For example, within a matter of minutes, a business analyst could train a clustering algorithm to group customers with similar demographics. The analyst could then assess which groups or segments are most likely to respond to a specific marketing campaign and then write the segmentation results back to an enterprise CRM system. The marketing team could then access real-time data on a customer’s likelihood to respond to a campaign.