# comparing Gaussian mixtures and k-means

## Summary

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.

## Context

This concept has the prerequisites:

- mixture of Gaussians models
- k-means
- Expectation-Maximization algorithm (K-means is analogous to the EM algorithm for fitting Gaussian mixtures.)

## 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)

## -Free-

→ Bayesian Reasoning and Machine Learning

## -Paid-

→ 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 11.4.2.5-11.4.2.7, pages 352-355

## See also

- Other clustering methods include:
- naive Bayes , a probabilistic model for discrete data
- spectral clustering , for when the clusters don't have nice convex shapes