Description
- Consider any dataset that has more than two class labels. You can create your own or download any publicly available dataset.
- 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
- Maximum number of iterations
- Highest quality of cluster is reached.
- 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.
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