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