D-Index: Distance Searching Index for Metric Data Sets
Multimedia Tools and Applications
Algorithms for processing K-closest-pair queries in spatial databases
Data & Knowledge Engineering
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
A compact space decomposition for effective metric indexing
Pattern Recognition Letters
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Dynamic spatial approximation trees
Journal of Experimental Algorithmics (JEA)
Indexing high-dimensional data in dual distance spaces: a symmetrical encoding approach
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
ACM Transactions on Database Systems (TODS)
Improving the space cost of k-NN search in metric spaces by using distance estimators
Multimedia Tools and Applications
Solving similarity joins and range queries in metric spaces with the list of twin clusters
Journal of Discrete Algorithms
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Maximal metric margin partitioning for similarity search indexes
Proceedings of the 18th ACM conference on Information and knowledge management
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We investigated the problem of reducing the cost of searching for the k closest pairs in metric spaces. In general, a k-closest pair search method initializes the upper bound distance between the k closest pairs as infinity and repeatedly updates the upper bound distance whenever it finds pairs of objects whose distances are shorter than that distance. Furthermore, it prunes dissimilar pairs whose distances are estimated as longer than the upper bound distance based on the distances from the pivot to objects and the triangle inequality. The cost of a k-closest pair query is smaller for a shorter upper bound distance and a sparser distribution of distances between the pivot and objects. We propose a new divide-and-conquer-based k-closest pair search method in metric spaces, called Adaptive Multi-Partitioning (AMP). AMP repeatedly divides and conquers objects from the sparser distance-distribution space and speeds up the convergence of the upper bound distance before partitioning the denser space. As a result, AMP can prune many dissimilar pairs compared with ordinary divide-and-conquer-based method. We compare our method with other partitioning method and show that AMP reduces distances computations.