1. Consider any dataset that has more than two class labels. You can create your own or download any publicly available dataset.
(a) Perform K-Medoid Clustering selecting the best value of k and taking Euclidean distance as similarity measure. Check your algorithm with the following three conditions
i. Maximum number of iterations
ii. Highest quality of cluster is reached.
(b) Repeat the above question taking Manhattan distance as similarity measure and note the difference between the clusters as compared to that found in Q. a.
