See [this document](../Practicum.md) for general information about the practicums.
Learning objectives:
- Implementing K-Means and Bisecting K-Means clustering algorithms
- Implementing Hierarchical Agglomerative Clustering using different cluster proximities
- Visualizing clusters (scatterplots and dendrograms)
Task 1. Implementing K-Means clustering
- A set of 2D data points are given (generated artificially)
- Select the centroids initially randomly from the data points
- Repeat until the cluster assignments change for less than 1% of the data points
- Visualize the cluster assignments and centroids after each iteration
Task 2. Implementing Bisecting K-Means clustering
- Solve the previous task using the bisecting variant of K-Means
- Measure the quality of the resulting clustering in terms of Sum of Squared Error (SSE)
* How does it compare to the SSE using regular K-Means?
* How does it compare to the SSE of the 'true' clustering?
Task 3. Implementing Hierarchical Agglomerative Clustering
- Cluster the "Italian cities" dataset (from the lecture) using Hierarchical Agglomerative Clustering
- Implement the Single link (MIN), Complete link (MAX), and Group average methods for comparing cluster proximities
- Bonus: visualize the different clusterings using dendrograms
