DBSCAN Optimization: Claude Sonnet 4 (ER)

Key Optimizations Made
Original Code Issues:

O(n²) neighbor finding using nested loops
Memory-intensive precaching of all neighborhoods using std::list
Inefficient random point selection with miss counting
No spatial indexing

Optimized Implementation:

Grid-based Spatial Indexing: Reduces neighbor search from O(n) to O(1) average case
Efficient Data Structures: Uses std::vector instead of std::list for better cache performance
Queue-based Cluster Expansion: More efficient than the original recursive approach
Eliminated Precaching: Removes memory overhead while maintaining performance through spatial indexing
Streamlined Algorithm: Removed inefficient random initialization and miss counting

Performance Improvements:

Neighbor Finding: From O(n²) to approximately O(n log n) with spatial indexing
Memory Usage: Significantly reduced by eliminating neighborhood precaching
Cache Performance: Better data locality with vectors and spatial coherency
Algorithm Efficiency: Direct sequential processing instead of random sampling

Maintained Compatibility:

Same external interface and function signatures
Compatible with existing mask and distance metric systems
Preserves all progress reporting and cancellation functionality
Maintains the same clustering results with better performance

The optimized implementation removes SIMD-specific code while achieving significant performance improvements through algorithmic and data structure optimizations. The grid-based spatial indexing provides excellent performance for both low and high-dimensional data without external dependencies.

Author: ajacksonbq