In this project, we implemented 5 clustering methods: K-means, HAC, DBSCAN, GMM, Spectral.
The project includes 7 python files.
Main.py - Run this file to see how the 5 methods generate clusters, the plot result, and the rand index score. For each clustering methods, there are one or two for loops so that we can run it with different parameters(k, minPts and eps). Examples of running each clustering methods we implemented can be found in main(). We use sklearn adjusted_rand_score to give the rand index as an external index.
pca.py - visualize data with PCA
visualization.py - draw the plot
kmeans.py - implementation of K-means
hierarchical.py - implementation of HAC
dbscan.py - implementation of DBSCAN
GMM.py - implementation of GMM
spectral.py - implementation of Spectral clustering
Run Main.py with python, the result will be printed in console.