An algorithm for finding nearest neighbours in (approximately) constant average time
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Modern Information Retrieval
t-Spanners as a Data Structure for Metric Space Searching
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
CM-tree: A dynamic clustered index for similarity search in metric databases
Data & Knowledge Engineering
Parallel query processing on distributed clustering indexes
Journal of Discrete Algorithms
Solving similarity joins and range queries in metric spaces with the list of twin clusters
Journal of Discrete Algorithms
Counting distance permutations
Journal of Discrete Algorithms
Navigating k-nearest neighbor graphs to solve nearest neighbor searches
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Practical construction of k-nearest neighbor graphs in metric spaces
WEA'06 Proceedings of the 5th international conference on Experimental Algorithms
Fast approximate nearest-neighbor search with k-nearest neighbor graph
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
A unified approximate nearest neighbor search scheme by combining data structure and hashing
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Proximity searching consists in retrieving from a database, objects that are close to a query. For this type of searching problem, the most general model is the metric space, where proximity is defined in terms of a distance function. A solution for this problem consists in building an offline index to quickly satisfy online queries. The ultimate goal is to use as few distance computations as possible to satisfy queries, since the distance is considered expensive to compute. Proximity searching is central to several applications, ranging from multimedia indexing and querying to data compression and clustering. In this paper we present a new approach to solve the proximity searching problem. Our solution is based on indexing the database with the k-nearest neighbor graph (knng), which is a directed graph connecting each element to its k closest neighbors. We present two search algorithms for both range and nearest neighbor queries which use navigational and metrical features of the knng graph. We show that our approach is competitive against current ones. For instance, in the document metric space our nearest neighbor search algorithms perform 30% more distance evaluations than AESA using only a 0.25% of its space requirement. In the same space, the pivot-based technique is completely useless.