Clustering is an example of unsupervised machine learning, meaning that you do not know ahead of time what groups you are looking for — you want the algorithm to find those groups for you.

Unsupervised machine learning discovers patterns in unstructured data without specific goals. It's utilized in various sectors, enhancing services like streaming and social media suggestions ...

Unsupervised machine learning is a useful technology that helps organizations identify hidden customer groups and learn how to improve their tactics when used with K-means clustering.

While there are many more machine learning frameworks available than are mentioned in this article, the frameworks mentioned here are well-supported and robust, and will help users to succeed in their ...

Here are the differences between supervised, semi-supervised, and unsupervised learning -- and how each is valuable in the enterprise.

Unsupervised learning is used mainly to discover patterns and detect outliers in data today, but could lead to general-purpose AI tomorrow Despite the success of supervised machine learning and ...

So instead of fearing machine learning, organizations should learn how to use the technology to the best advantage while also understanding its limitations.

We propose using unsupervised clustering of the continuous output of machine learning models to provide discrete risk stratification for predicting time to first treatment in a cohort of patients with ...