In this project, we explore unsupervised learning algorithms
- Clustering and Dimensionality Reduction Algorithm are used
- k-means clustering
- Expectation Maximization
- PCA
- ICA
- Randomized Projections
- Any other feature selection algo you desire
- Phone Price Prediction
- Salary Prediction
These datasets are taken from Assignment 1
- Run the clustering algorithms on the datasets and describe what you see.
- Apply the dimensionality reduction algorithms to the two datasets and describe what you see.
- Reproduce your clustering experiments, but on the data after you've run dimensionality reduction on it. Yes, that’s 16 combinations of datasets, dimensionality reduction, and clustering method. You should look at all of them, but focus on the more interesting findings in your report.
- Apply the dimensionality reduction algorithms to one of your datasets from assignment #1 (if you've reused the datasets from assignment #1 to do experiments 1-3 above then you've already done this) and rerun your neural network learner on the newly projected data.
- Apply the clustering algorithms to the same dataset to which you just applied the dimensionality reduction algorithms (you've probably already done this), treating the clusters as if they were new features. In other words, treat the clustering algorithms as if they were dimensionality reduction algorithms. Again, rerun your neural network learner on the newly projected data.