A3: Unsupervised learning with PCA, t-SNE, k-means, AHC and SOM

Datasets (input)

The unsupervised learning techniques are applied on two datasets:

1. A3-data.txt
2. Wine.txt

Principal Component Analysis (PCA)

Source code of PCA:

1. PCA_d1.ipynb
2. PCA_d2.ipynb

t-distributed Neighbor Stochastic Embedding (t-SNE)

Source code of t-SNE:

1. t-SNE_d1.ipynb
2. t-SNE_d2.ipynb

k-means

Source code of k-means:

1. k-means_d1.ipynb
2. k-means_d2.ipynb

Agglomerative Hierarchical Clustering (AHC)

Source code of AHC:

1. AHC_d1.ipynb
2. AHC_d2.ipynb

Self-Organizing Maps (SOM)

Source code of SOM:

1. SOM_d1.ipynb
2. SOM_d2.ipynb

Documentation

• A3_report.pdf