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 184.108.40.206-220.127.116.11, pages 352-355
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