# 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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://wilmerags.gitbook.io/digital-garden/ai/ml/unsupervised_learning.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.