Add Notebook for K-Medoids Clustering

[Cgarg9]

Description:
The repository currently lacks coverage of K-Medoids clustering, a clustering technique similar to K-Means but more robust to noise and outliers. This method selects actual data points as cluster centers, making it particularly useful for categorical data.

Tasks:

• Create a new notebook explaining the K-Medoids algorithm.
• Include examples with sample datasets.
• Add a comparison with K-Means to highlight the differences.
• Name the notebook k_medoids_clustering.ipynb.
• Update the README file with a link to any resources used.