Vorono trees and clustering problems
Information Systems
ACM Computing Surveys (CSUR)
Modern Information Retrieval
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
A Probabilistic Spell for the Curse of Dimensionality
ALENEX '01 Revised Papers from the Third International Workshop on Algorithm Engineering and Experimentation
Data Structures and Efficient Algorithms, Final Report on the DFG Special Joint Initiative
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
An Effective Clustering Algorithm to Index High Dimensional Metric Spaces
SPIRE '00 Proceedings of the Seventh International Symposium on String Processing Information Retrieval (SPIRE'00)
A Data Structure and an Algorithm for the Nearest Point Problem
IEEE Transactions on Software Engineering
t-Spanners as a Data Structure for Metric Space Searching
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Antipole Tree Indexing to Support Range Search and K-Nearest Neighbor Search in Metric Spaces
IEEE Transactions on Knowledge and Data Engineering
Dynamic spatial approximation trees
Journal of Experimental Algorithmics (JEA)
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The main bottleneck of the research in metric space searching is the so-called curse of dimensionality, which makes the task of searchingsome metric spaces intrinsically difficult, whatever algorithm is used. A recent trend to break this bottleneck resorts to probabilistic algorithms, where it has been shown that one can find 99% of the elements at a fraction of the cost of the exact algorithm. These algorithms are welcome in most applications because resortingto metric space searching already involves a fuzziness in the retrieval requirements. In this paper we push further in this direction by developingp robabilistic algorithms on data structures whose exact versions are the best for high dimensions. As a result, we obtain probabilistic algorithms that are better than the previous ones. We also give new insights on the problem and propose a novel view based on time-bounded searching.