comparing Gaussian mixtures and k-means
Gaussian mixture models and K-means are two canonical approaches to clustering, i.e. dividing data points into meaningful groups. This concept node discusses the tradeoffs between them.
This concept has the prerequisites:
Core resources (we're sorry, we haven't finished tracking down resources for this concept yet)
Supplemental resources (the following are optional, but you may find them useful)
→ Bayesian Reasoning and Machine Learning
A textbook for a graudate machine learning course.
Location: Section 20.3.5, "K-means," page 413
→ Pattern Recognition and Machine Learning
A textbook for a graduate machine learning course, with a focus on Bayesian methods.
Location: Section 9.3.2, pages 443-444
→ Machine Learning: a Probabilistic Perspective
A very comprehensive graudate-level machine learning textbook.
Location: Section 18.104.22.168-22.214.171.124, pages 352-355
- create concept: shift + click on graph
- change concept title: shift + click on existing concept
- link together concepts: shift + click drag from one concept to another
- remove concept from graph: click on concept then press delete/backspace
- add associated content to concept: click the small circle that appears on the node when hovering over it
- other actions: use the icons in the upper right corner to optimize the graph placement, preview the graph, or download a json representation