Glossary

Unsupervised Machine Learning

What is Unsupervised Machine Learning?

Unsupervised Machine Learning discovers patterns within an existing set of data without pre-existing labels or categories. Generally, unsupervised learning evaluates the data to find clusters with a similar set of values sufficiently different than other clusters, enabling new data to be categorized into an existing cluster.

 

Why is Unsupervised Machine Learning important?

Unsupervised Machine Learning has a couple of advantages over supervised learning, in that it does not require a labeled set of training data, and that it can discover unknown or unexpected relationships in data not previously identified by human observation. This is particularly useful when trying to characterize subsegments or microsegments of a population to discover buying behavior or health issues using a large number of input variables.

 

How C3 AI Enables Organizations to Use Unsupervised Machine Learning

C3 AI makes it easy to apply unsupervised machine learning models 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. The C3 AI ML Pipelines feature enables data scientists to easily build pipelines of different models to create complex analytic workflows, combining techniques from different domains to produce a more sophisticated result. For example, one could use image recognition to translate a complex diagram into its constituent components and labels, then match the labels to a database bill of materials, connect to available sensor data, build a model to predict potential failures hours or days in advance, and create work orders for provisioning replacement parts to address the issue during a maintenance window.