Add K-Means & K-Medians clustering algorithm

[Ashish2343]

Add K-Means and K-Medians clustering (machinelearning package)

Adds a new machinelearning package with Clustring, providing two centroid-based clustering algorithms that share one iterative core (assign → update → check convergence):

• K-Means — squared Euclidean distance, centers = mean
• K-Medians — Manhattan distance, centers = median (more robust to outliers)

Both support deterministic initial centers (for reproducible tests) or seeded random init. Includes input validation (empty dataset, invalid k, ragged dimensions, mismatched center count) and a full JUnit 5 test suite (ClustringTest) covering both algorithms, edge cases (k=1, k=n), and an outlier-robustness comparison.

Closes #7534

[codecov-commenter]

Codecov Report

❌ Patch coverage is 94.28571% with 8 lines in your changes missing coverage. Please review.
✅ Project coverage is 80.51%. Comparing base (7c934ad) to head (fde018a).

Files with missing lines	Patch %	Lines
.../com/thealgorithms/machinelearning/Clustering.java	94.28%	2 Missing and 6 partials ⚠️

Additional details and impacted files

@@             Coverage Diff              @@
##             master    #7552      +/-   ##
============================================
+ Coverage     80.42%   80.51%   +0.09%     
- Complexity     7457     7507      +50     
============================================
  Files           815      816       +1     
  Lines         24055    24195     +140     
  Branches       4732     4763      +31     
============================================
+ Hits          19346    19481     +135     
- Misses         3945     3946       +1     
- Partials        764      768       +4     

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[alxkm]

@Ashish2343, before a detailed review, the Clang formatter needs to be fixed.

And few quick suggestions regarding code.

• Typo in the test class name: class ClustringTest inside ClusteringTest.java
• Missing Javadoc on one overload: kMeans(double[][], int k, long seed, int, double) - the other three have it. Copy the wording from the seeded kMedians.
• The empty-cluster branch in updateCenters isn't covered. One test is enough.
• Variables centroids vs centers. Right now k-means takes initialCentroids, k-medians takes initialCenters, the shared internals say centers, yet the helpers are named validateAndCopyCentroids / randomInitialCentroids even though both paths call them. Terminologically "centroid" means the mean, so it's wrong for medians. Pick centers as the shared vocabulary (internals + helpers) and keep centroids only in the public k-means signature