2D K-Means Clustering Implementation Code

In the current era of data science development, clustering techniques have become increasingly prevalent. While 1D k-means clustering is commonly available, real-world applications often involve multi-dimensional data that requires more sophisticated algorithms. This implementation provides 2D k-means clustering code that handles two-dimensional data points using the standard k-means algorithm approach: initializing centroids, assigning points to nearest clusters, and iteratively updating centroids until convergence. Although originally developed by international programmers, the code features comprehensive documentation and can be readily adapted for project integration with minor adjustments. The algorithm employs Euclidean distance calculations for cluster assignment and includes convergence checks to ensure optimal clustering results. This implementation is believed to offer new perspectives and methodologies for enhanced data processing capabilities in multidimensional analysis scenarios.