Multiattribute hashing using Gray codes
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
Fractals for secondary key retrieval
PODS '89 Proceedings of the eighth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Linear clustering of objects with multiple attributes
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Finding k farthest pairs and k closest/farthest bichromatic pairs for points in the plane
SCG '92 Proceedings of the eighth annual symposium on Computational geometry
Approximate nearest neighbor queries revisited
SCG '97 Proceedings of the thirteenth annual symposium on Computational geometry
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Closest pair queries in spatial databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Clustering Algorithms
High Dimensional Similarity Search With Space Filling Curves
Proceedings of the 17th International Conference on Data Engineering
C2P: Clustering based on Closest Pairs
Proceedings of the 27th International Conference on Very Large Data Bases
Divide-and-conquer in multidimensional space
STOC '76 Proceedings of the eighth annual ACM symposium on Theory of computing
Introduction to mathematical techniques in pattern recognition
Introduction to mathematical techniques in pattern recognition
Global Optimization with Non-Convex Constraints - Sequential and Parallel Algorithms (Nonconvex Optimization and its Applications Volume 45) (Nonconvex Optimization and Its Applications)
Enumerating the k closest pairs optimally
SFCS '92 Proceedings of the 33rd Annual Symposium on Foundations of Computer Science
An approximate algorithm for top-k closest pairs join query in large high dimensional data
Data & Knowledge Engineering
Solving similarity joins and range queries in metric spaces with the list of twin clusters
Journal of Discrete Algorithms
Self-Describing context-based pixel ordering
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
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An approximate algorithm to efficiently solve the &i:k&ei:-Closest-Pairs problem in high-dimensional spaces is presented. The method is based on dimensionality reduction of the space Rd through the Hilbert space filling curve and performs at most d+1 scans of the data set. After each scan, those points whose contribution to the solution has already been analyzed, are eliminated from the data set. The pruning is lossless, in fact the remaining points along with the approximate solution found can be used for the computation of the exact solution. Although we are able to guarantee an O(d1+1/t) approximation to the solution, where t = 1, ..., 驴 denotes the used Lt metric, experimental results give the exact k-Closest-Pairs for all the data sets considered and show that the pruning of the search space is effective.