An algorithm for finding nearest neighbours in (approximately) constant average time
Pattern Recognition Letters
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
ACM Computing Surveys (CSUR)
Low Redundancy in Static Dictionaries with Constant Query Time
SIAM Journal on Computing
t-Spanners as a Data Structure for Metric Space Searching
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
A compact space decomposition for effective metric indexing
Pattern Recognition Letters
Engineering efficient metric indexes
Pattern Recognition Letters
On the least cost for proximity searching in metric spaces
WEA'06 Proceedings of the 5th international conference on Experimental Algorithms
Optimal Pivots to Minimize the Index Size for Metric Access Methods
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
Indexing methods for approximate dictionary searching: Comparative analysis
Journal of Experimental Algorithmics (JEA)
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We consider indexing and range searching in metric spaces. The best method known is AESA, in practice requiring the fewest number of distance evaluations to answer range queries. The problem with AESA is its space complexity, requiring storage for Θ(n2) distance values to index n objects.We give several methods to reduce this cost. The main observation is that exact distance values are not needed, but lower and upper bounds suffice. The simplest of our methods need only Θ(n2) bits (as opposed to words) of storage, but the price to pay is more distance evaluations, the exact cost depending on the dimension, as compared to AESA. To reduce this efficiency gap we extend our method to use b distance bounds, requiring Θ(n2 log2(b)) bits of storage. The scheme uses also Θ(b) or Θ(bn) words of auxiliary space. We experimentally show that using b ∈ {1, . . . , 16} (depending on the problem instance) gives good results. Our preprocessing and side computation costs are the same as for AESA. We propose several improvements, achieving e.g. O(n1+α) construction cost for some 0