SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Efficient geometry-based similarity search of 3D spatial databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
Similarity Search without Tears: The OMNI Family of All-purpose Access Methods
Proceedings of the 17th International Conference on Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
D-Index: Distance Searching Index for Metric Data Sets
Multimedia Tools and Applications
Depth-First K-Nearest Neighbor Finding Using the MaxNearestDist Estimator
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
ACM SIGGRAPH 2004 Papers
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
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)
Finding the k-closest pairs in metric spaces
Proceedings of the 1st Workshop on New Trends in Similarity Search
Large-scale similarity-based join processing in multimedia databases
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
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Similarity searching in metric spaces has a vast number of applications in several fields like multimedia databases, text retrieval, computational biology, and pattern recognition. In this context, one of the most important similarity queries is the k nearest neighbor (k-NN) search. The standard best-first k-NN algorithm uses a lower bound on the distance to prune objects during the search. Although optimal in several aspects, the disadvantage of this method is that its space requirements for the priority queue that stores unprocessed clusters can be linear in the database size. Most of the optimizations used in spatial access methods (for example, pruning using MinMaxDist) cannot be applied in metric spaces, due to the lack of geometric properties. We propose a new k-NN algorithm that uses distance estimators, aiming to reduce the storage requirements of the search algorithm. The method stays optimal, yet it can significantly prune the priority queue without altering the output of the query. Experimental results with synthetic and real datasets confirm the reduction in storage space of our proposed algorithm, showing savings of up to 80% of the original space requirement.