| description |
|---|
Unsupervised learning knowledge and experiences |
One thing to note is that it’s often easiest to form clusters when you have low-dimensional data. For example, it can be difficult, and often noisy, to get good clusters from data that has over 100 features. In high-dimensional cases, there is often a dimensionality reduction step that takes place before data is analyzed by a clustering algorithm.