Implement K-Medoids Clustering Algorithm #13488

[Nikita-Kedari]

Describe your change:

This PR implements the K-Medoids clustering algorithm using pure Python and NumPy on the Iris dataset.

Key features:

• Iterative medoid updates to assign points to the nearest medoid
• Returns final medoids and cluster assignments
• Handles random initialization with optional seed
• Optional visualization code included but commented out

Fixes #13488

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".

[Nikita-Kedari]

@cclauss Please could you guide me further for this PR. It has passed all 4 tests. Please let me know on any further changes. Thank you!

[Nikita-Kedari]

Hi Maintainers! Please let me know if any changes are required. Please guide for any further updations. Thanks you so much.

[Nikita-Kedari]

@cclauss Please could you review this PR? Let me know for any changes required I will be happy to work on it.