Spectral Clustering (with hierarchical clustering) and K-means

About

This project is implemented on a toy dataset for K-means algorithm and Spectral Clustering Algorithm with hierarchical clustering.

Instructions to run the code:

1. Clone the repository using

git clone https://github.com/Abhishek-Nalawade/Spectral-Clustering-and-K-means

2. Run the code Execute_comparison.py to compare both the methods for a certain number of classes (2, 4 or 8).
3. Run the code plot_costs.py to plot the costs for both methods for 3 different nmuber of classes (2, 4 or 8) at once. The plot_costs.py code measures the cost as a result of the clusters formed using a criterion function.

Results:

It can be seen that for the dataset with circular trends Kmeans fails to cluster the data correctly whereas Spectral Clustering does it effectively

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Clustering with 2 classes

Clustering with 4 classes

Clustering with 8 classes

Notes:

1. The results obtained from K-means are not always consistent, because of the random initialization of the initial centroids the clusters formed can be different.