Learning outcomes: Chapter 8 - clustering

At the end of this module, students will be able to:

  • Describe the goals of clustering
  • Describe principles that define the quality of clusters
  • Describe the difference between data points and features
  • Implement Lloyd's algorithm for k-means clustering
  • Evaluate the runtime and convergence of Lloyd's k-means algorithm
  • Describe situations where k-means clustering is ineffective
  • Describe the difference between hierarchical and k-means clustering
  • Implement a hierarchical clustering approach
  • Convert a set of data points into a distance matrix suitable for hierarchical clustering
  • Describe the different "flavors" of hierarchical clustering
  • Apply one of the commonly-used hierarchical clustering algorithms to construct a hierarchical clustering of a set of points