Description

This pull request adds an implementation of the K-Means clustering algorithm using Lloyd's algorithm.

Summary

• Implements the K-Means clustering algorithm for multidimensional data.
• Assigns each point to its nearest centroid using squared Euclidean distance.
• Recomputes centroid positions until convergence or the maximum number of iterations is reached.
• Preserves the previous centroid when a cluster becomes empty.
• Includes comprehensive input validation for invalid arguments.
• Adds a corresponding JUnit test class covering both normal and edge cases.

Validation

The implementation validates:

• Null input arrays
• Empty datasets
• Empty centroid arrays
• More centroids than data points
• Non-positive maximum iterations
• Negative tolerance
• Dimension mismatches
• Zero-dimensional points
• Null point and centroid rows

Tests

The accompanying test class covers:

• Simple clustering
• Single-cluster datasets
• Immediate convergence
• Empty-cluster handling
• Null inputs
• Empty datasets
• Empty centroid arrays
• Invalid iteration counts
• Negative tolerance
• Too many centroids
• Dimension mismatches
• Zero-dimensional points

Checklist

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized it.
• All filenames are in PascalCase.
• All functions and variable names follow Java naming conventions.
• All new algorithms have a URL in their comments that points to Wikipedia or other similar explanations.
• All new algorithms include a corresponding test class that validates their functionality.
• All new code is formatted with clang-format -i --style=file path/to/your/file.java.