---
description: Unsupervised learning knowledge and experiences
---

# Unsupervised\_learning

## Clustering

### Data Dimensionality

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.
