- Randomly built KD-Tree
- Optimal (balanced) KD-Tree using median-based splitting
- Dynamic insertion of k-dimensional points
- Insertion queries on existing trees
- Exact match query
- Partial match query
- Region (range) query
- Nearest Neighbor (NN) query
- Radius query
- Deletion of points from the KD-Tree
- Dimension-aware restructuring after deletion
| Operation | Description |
|---|---|
| Insert | Insert a k-dimensional point |
| Search | Search for a point in the KD-Tree |
| Exact Match | Find an exact point |
| Partial Match | Match on selected dimensions |
| Region Query | Range search within a hyper-rectangle |
| NN Query | Nearest neighbor search |
| Radius Query | Points within a given radius |
| Delete | Remove a point from the tree |
- Language: C++
- Dimensionality: Generic k dimensions
- Distance Metric: Euclidean distance
- Tree Type: KD-Tree
- Construction Methods:
- Random insertion
- Median-based optimal construction