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
Some approaches to best-match file searching
Communications of the ACM
ACM Computing Surveys (CSUR)
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
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Proximity Matching Using Fixed-Queries Trees
CPM '94 Proceedings of the 5th Annual Symposium on Combinatorial Pattern Matching
Pivot selection techniques for proximity searching in metric spaces
Pattern Recognition Letters
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Selecting vantage objects for similarity indexing
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Similarity Search Using Sparse Pivots for Efficient Multimedia Information Retrieval
ISM '06 Proceedings of the Eighth IEEE International Symposium on Multimedia
A Data Structure and an Algorithm for the Nearest Point Problem
IEEE Transactions on Software Engineering
Reference-based indexing for metric spaces with costly distance measures
The VLDB Journal — The International Journal on Very Large Data Bases
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Searching in metric spaces is a very active field since it offers methods for indexing and searching by similarity in collections of unstructured data. These methods select some objects of the collection as reference objects to build the indexes. It has been shown that the way the references are selected affects the search performance, and several algorithms for good reference selection have been proposed. Most of them assume the space to have a reasonably regular distribution. However, in some spaces the objects are grouped in small dense clusters that can make these methods perform worse than a random selection. In this paper, we propose a new method able to detect these situations and adapt the structure of the index to them. Our experimental evaluation shows that our proposal is more efficient than previous approaches when using the same amount of memory.