Foundations of Multidimensional and Metric Data Structures¶
Samet, H. (2006). Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann.
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Primes¶
- Metric
- The triangle inequality is the workhorse of this compression: if a is close to b and b is close to c, then a cannot be far from c, and that bound is what makes nearest-neighbour search, hierarchical clustering, and metric indexing — KD-trees, ball-trees, M-trees — tractable at scale.
This sourceComprehensive reference on metric indexing (KD-trees, ball-trees, M-trees) and triangle-inequality pruning for similarity search.
- The triangle inequality is the workhorse of this compression: if a is close to b and b is close to c, then a cannot be far from c, and that bound is what makes nearest-neighbour search, hierarchical clustering, and metric indexing — KD-trees, ball-trees, M-trees — tractable at scale.
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Registry ID ref:3855da726537 · see in the full table