Resources

Readings

Notation

• Notation Guide PDF
  • Guide that describes the notation described in several of the textbooks and webpages listed below.
  • AMIA, ESL, ISL, ItML, OMP, WKMC

Textbooks

• [AIMA] Russell and Norvig. Artificial Intelligence: a modern approach
  • Textbook;
  • No k-means coverage - only EM
• [ESL] Hastie, Tibshirani, and Friedman. The Elements of Statistical Learning (PDF)
  • Textbook, free online;
  • Section 14.3.6, with mathematical context to problem
• [ISL] James, Witten, Hastie, Tibshirani. An Introduction to Statistical Learning with Applications in R (PDF)
  • Textbook, free online;
  • Section 10.3.1, more explanatory than [ESL], lacks discussion of how to choose k
  • Includes R lab for experimenting with k-means, Section 10.5.1
• [ItML] Alpaydin. Introduction to Machine Learning (Lecture Slides PDF and PPT)
  • Textbook;
  • brief coverage of k-means

General References

• [OMP] http://www.onmyphd.com/?p=k-means.clustering
  • Website;
  • Interactive visualization of algorithms, aids in understanding strengths and weaknesses of method
• [WKMC] Wikipedia - K-Means Clustering
  • Website;
  • basic definitions, list of online resources, implementations, etc.
• [WUL] Wikipedia - Unsupervised Learning
  • Website;
  • succinct definitions
• [WDTNOC] Wikipedia - Determining the number of clusters in a data set
  • Website;
  • less detailed than ESL 14.3.11
• [WJM] Wikipedia - K-Medoids
  • Website;
  • concise, clear, with visual example where K-means fails and K-medoids succeeds

Online Resources

• Links available at WKMC 8 includes links to software (some open-source), visualizations, demos, etc.
• Video illustrating Voronoi partitioning with uniform distribution YouTube
• Teaching video: YouTube

MOOC Resources

Here is a sampling of some of the MOOC courses that have some coverage of clustering. Note, there are many other courses that may contain material relating to clustering and k-means clustering.

• Coursera, Machine Learning, Stanford University
  Andrew Ng
  Free, rolling enrollment through the year
  https://www.coursera.org/learn/machine-learning
  The upcoming course covers unsupervised learning and dimensionality reduction in Week 8.

• Coursera, Cluster Analysis in Data Mining, University of Illinois at Urbana-Champaign
  Jiawei Han
  Part of Data Mining Specialization
  Archive of course from April-June 2015
  https://www.coursera.org/specialization/datamining/20?utm_medium=courseDescripTop
  https://www.coursera.org/course/clusteranalysis

• Coursera, Clustering & Retrieval, University of Washington
  Emily Fox, Carlos Guestrin
  Part of the Machine Learning Specialization, starts at $79, enroll for Feb. 2016
  https://www.coursera.org/specializations/machine-learning
  https://www.coursera.org/learn/ml-clustering-and-retrieval
  Projects related to document retrieval; cluster by topics

• edX, MITx: 15.071x The Analytics Edge, Massachusetts Institute of Technology
  Multiple instructors
  Free, course archive https://courses.edx.org/courses/course-v1:MITx+15.071x_2a+2T2015/info
  Project of using clustering documents in R

• Udacity, Machine Learning, Georgia Tech
  Michael Littman, Charles Isbell
  Free, course archive https://www.udacity.com/course/machine-learning--ud262