1. Implement a K-means++ function and test it on synthetic data.
2. Write a spectral clustering function and test it on synthetic data
3. Demonstrate that you can use the \elbow" method or Silhouette in order to choose k correctly for synthetic data
4. Apply your K-means and spectral clustering to the microarray data set. Use K-selection methods to find signifcant clusters. Visualize the results and the selection process
5. Familiarize yourself with the t-SNE algorithm using MNIST and a toy dataset
You find the implemented code at code/Clustering.py
Check out the results.pdf